

%feature("docstring") NPasteImageFilter "

Paste an image (or a constant value) into another image.


NPasteImageFilter allows a region in a destination image to be filled with a source
image or a constant pixel value. The SetDestinationIndex() method
prescribes where in the destination input to start pasting data from
the source input. The SetSourceRegion method prescribes the section of
the second image to paste into the first. When a constant pixel value
is set, the SourceRegion describes the size of the region filled. If
the output requested region does not include the SourceRegion after it
has been repositioned to DestinationIndex, then the output will just
be a copy of the input.

This filter supports running \"InPlace\" to efficiently reuses the
destination image buffer for the output, removing the need to copy the
destination pixels to the output.

When the source image is a lower dimension than the destination image
then the DestinationSkipAxes parameter specifies which axes in the
destination image are set to 1 when copying the region or filling with
a constant.

C++ includes: sitkPasteImageFilter.h
";


%feature("docstring") itk::HashImageFilter "

Generates a hash string from an image.



This class utlizes low level buffer pointer access, to work with itk::Image and itk::VectorImage. It is modeled after the access an ImageFileWriter provides to an ImageIO.
Todo
Update in-place on to default after fixing bug in InPlaceImageFilter


C++ includes: itkHashImageFilter.h
";

%feature("docstring")  itk::HashImageFilter::GetHash "

Get the computed Hash values

";

%feature("docstring")  itk::HashImageFilter::GetHashOutput "
";

%feature("docstring")  itk::HashImageFilter::GetHashOutput "
";

%feature("docstring")  itk::HashImageFilter::itkGetMacro "
";

%feature("docstring")  itk::HashImageFilter::itkNewMacro "

Method for creation through the object factory.

";

%feature("docstring")  itk::HashImageFilter::itkSetMacro "

Set/Get hashing function as enumerated type

";

%feature("docstring")  itk::HashImageFilter::itkTypeMacro "

Runtime information support.

";

%feature("docstring")  itk::HashImageFilter::MakeOutput "
";


%feature("docstring") itk::HolderCommand "

An ITK Command class to hold a object until destruction.


This command is to add resource management, by utilizing the lifetime
of a Command added to an object is about the same as that managed object. So this
command holds onto a resource or object for lifetime of itself. By
adding as a command to an ITK object it will be released on
destruction of the ITK object ( subject to the reference counting on
the Command ).

C++ includes: itkHolderCommand.h
";

%feature("docstring")  itk::HolderCommand::Execute "
";

%feature("docstring")  itk::HolderCommand::Execute "
";

%feature("docstring")  itk::HolderCommand::Get "
";

%feature("docstring")  itk::HolderCommand::Get "
";

%feature("docstring")  itk::HolderCommand::HolderCommand "
";

%feature("docstring")  itk::HolderCommand::itkNewMacro "
";

%feature("docstring")  itk::HolderCommand::Set "
";


%feature("docstring") itk::HolderCommand< T * > "
C++ includes: itkHolderCommand.h
";

%feature("docstring")  itk::HolderCommand< T * >::Execute "
";

%feature("docstring")  itk::HolderCommand< T * >::Execute "
";

%feature("docstring")  itk::HolderCommand< T * >::Get "
";

%feature("docstring")  itk::HolderCommand< T * >::Get "
";

%feature("docstring")  itk::HolderCommand< T * >::HolderCommand "
";

%feature("docstring")  itk::HolderCommand< T * >::itkNewMacro "
";

%feature("docstring")  itk::HolderCommand< T * >::Set "
";


%feature("docstring") itk::ImageIOFactoryRegisterManager "
C++ includes: itkImageIOFactoryRegisterManager.h
";

%feature("docstring")  itk::ImageIOFactoryRegisterManager::ImageIOFactoryRegisterManager "
";


%feature("docstring") itk::TransformIOFactoryRegisterManager "
C++ includes: itkTransformIOFactoryRegisterManager.h
";

%feature("docstring")  itk::TransformIOFactoryRegisterManager::TransformIOFactoryRegisterManager "
";


%feature("docstring") itk::simple::AbsImageFilter "

Computes the absolute value of each pixel.


itk::Math::abs() is used to perform the computation.
See:
 itk::simple::Abs for the procedural interface

 itk::AbsImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAbsImageFilter.h
";

%feature("docstring")  itk::simple::AbsImageFilter::AbsImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AbsImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::AbsImageFilter::Execute "
";

%feature("docstring")  itk::simple::AbsImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AbsImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AbsImageFilter::~AbsImageFilter "

Destructor

";


%feature("docstring") itk::simple::AbsoluteValueDifferenceImageFilter "

Implements pixel-wise the computation of absolute value difference.


This filter is parameterized over the types of the two input images
and the type of the output image.

Numeric conversions (castings) are done by the C++ defaults.

The filter will walk over all the pixels in the two input images, and
for each one of them it will do the following:


Cast the input 1 pixel value to double .

Cast the input 2 pixel value to double .

Compute the difference of the two pixel values.

Compute the absolute value of the difference.

Cast the double value resulting from the absolute value to the pixel
type of the output image.

Store the casted value into the output image.
 The filter expects all images to have the same dimension (e.g. all
2D, or all 3D, or all ND).
See:
 itk::simple::AbsoluteValueDifference for the procedural interface

 itk::AbsoluteValueDifferenceImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAbsoluteValueDifferenceImageFilter.h
";

%feature("docstring")  itk::simple::AbsoluteValueDifferenceImageFilter::AbsoluteValueDifferenceImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AbsoluteValueDifferenceImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::AbsoluteValueDifferenceImageFilter::Execute "
";

%feature("docstring")  itk::simple::AbsoluteValueDifferenceImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::AbsoluteValueDifferenceImageFilter::Execute "
";

%feature("docstring")  itk::simple::AbsoluteValueDifferenceImageFilter::Execute "
";

%feature("docstring")  itk::simple::AbsoluteValueDifferenceImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AbsoluteValueDifferenceImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AbsoluteValueDifferenceImageFilter::~AbsoluteValueDifferenceImageFilter "

Destructor

";


%feature("docstring") itk::simple::AcosImageFilter "

Computes the inverse cosine of each pixel.


This filter is templated over the pixel type of the input image and
the pixel type of the output image.

The filter walks over all the pixels in the input image, and for each
pixel does do the following:


cast the pixel value to double ,

apply the std::acos() function to the double value

cast the double value resulting from std::acos() to the pixel type of
the output image

store the casted value into the output image.
 The filter expects both images to have the same dimension (e.g. both
2D, or both 3D, or both ND).
See:
 itk::simple::Acos for the procedural interface

 itk::AcosImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAcosImageFilter.h
";

%feature("docstring")  itk::simple::AcosImageFilter::AcosImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AcosImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::AcosImageFilter::Execute "
";

%feature("docstring")  itk::simple::AcosImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AcosImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AcosImageFilter::~AcosImageFilter "

Destructor

";


%feature("docstring") itk::simple::AdaptiveHistogramEqualizationImageFilter "

Power Law Adaptive Histogram Equalization.


Histogram equalization modifies the contrast in an image. The AdaptiveHistogramEqualizationImageFilter is a superset of many contrast enhancing filters. By modifying its
parameters (alpha, beta, and window), the AdaptiveHistogramEqualizationImageFilter can produce an adaptively equalized histogram or a version of unsharp
mask (local mean subtraction). Instead of applying a strict histogram
equalization in a window about a pixel, this filter prescribes a
mapping function (power law) controlled by the parameters alpha and
beta.

The parameter alpha controls how much the filter acts like the
classical histogram equalization method (alpha=0) to how much the
filter acts like an unsharp mask (alpha=1).

The parameter beta controls how much the filter acts like an unsharp
mask (beta=0) to much the filter acts like pass through (beta=1, with
alpha=1).

The parameter window controls the size of the region over which local
statistics are calculated. The size of the window is controlled by
SetRadius the default Radius is 5 in all directions.

By altering alpha, beta and window, a host of equalization and unsharp
masking filters is available.

The boundary condition ignores the part of the neighborhood outside
the image, and over-weights the valid part of the neighborhood.

For detail description, reference \"Adaptive Image Contrast
Enhancement using Generalizations of Histogram Equalization.\" J.Alex
Stark. IEEE Transactions on Image Processing, May 2000.
See:
 itk::simple::AdaptiveHistogramEqualization for the procedural interface

 itk::AdaptiveHistogramEqualizationImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAdaptiveHistogramEqualizationImageFilter.h
";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::AdaptiveHistogramEqualizationImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::GetAlpha "

Set/Get the value of alpha. Alpha = 0 produces the adaptive histogram
equalization (provided beta=0). Alpha = 1 produces an unsharp mask.
Default is 0.3.

";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::GetBeta "

Set/Get the value of beta. If beta = 1 (and alpha = 1), then the
output image matches the input image. As beta approaches 0, the filter
behaves as an unsharp mask. Default is 0.3.

";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::SetAlpha "

Set/Get the value of alpha. Alpha = 0 produces the adaptive histogram
equalization (provided beta=0). Alpha = 1 produces an unsharp mask.
Default is 0.3.

";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::SetBeta "

Set/Get the value of beta. If beta = 1 (and alpha = 1), then the
output image matches the input image. As beta approaches 0, the filter
behaves as an unsharp mask. Default is 0.3.

";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AdaptiveHistogramEqualizationImageFilter::~AdaptiveHistogramEqualizationImageFilter "

Destructor

";


%feature("docstring") itk::simple::AddImageFilter "

Pixel-wise addition of two images.


This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.

The pixel type of the input 1 image must have a valid definition of
the operator+ with a pixel type of the image 2. This condition is
required because internally this filter will perform the operation


Additionally the type resulting from the sum, will be cast to the
pixel type of the output image.

The total operation over one pixel will be

For example, this filter could be used directly for adding images
whose pixels are vectors of the same dimension, and to store the
resulting vector in an output image of vector pixels.

The images to be added are set using the methods:

Additionally, this filter can be used to add a constant to every pixel
of an image by using


WARNING:
No numeric overflow checking is performed in this filter.

See:
 itk::simple::Add for the procedural interface

 itk::AddImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAddImageFilter.h
";

%feature("docstring")  itk::simple::AddImageFilter::AddImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AddImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::AddImageFilter::Execute "
";

%feature("docstring")  itk::simple::AddImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::AddImageFilter::Execute "
";

%feature("docstring")  itk::simple::AddImageFilter::Execute "
";

%feature("docstring")  itk::simple::AddImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AddImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AddImageFilter::~AddImageFilter "

Destructor

";


%feature("docstring") itk::simple::AdditiveGaussianNoiseImageFilter "

Alter an image with additive Gaussian white noise.


Additive Gaussian white noise can be modeled as:


$ I = I_0 + N $

where $ I $ is the observed image, $ I_0 $ is the noise-free image and $ N $ is a normally distributed random variable of mean $ \\\\mu $ and variance $ \\\\sigma^2 $ :

$ N \\\\sim \\\\mathcal{N}(\\\\mu, \\\\sigma^2) $
 The noise is independent of the pixel intensities.


Gaetan Lehmann
 This code was contributed in the Insight Journal paper \"Noise
Simulation\". https://hdl.handle.net/10380/3158
See:
 itk::simple::AdditiveGaussianNoise for the procedural interface

 itk::AdditiveGaussianNoiseImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAdditiveGaussianNoiseImageFilter.h
";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::AdditiveGaussianNoiseImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::Execute "
";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::GetMean "

Set/Get the mean of the Gaussian distribution. Defaults to 0.0.

";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::GetSeed "
";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::GetStandardDeviation "

Set/Get the standard deviation of the Gaussian distribution. Defaults
to 1.0.

";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::SetMean "

Set/Get the mean of the Gaussian distribution. Defaults to 0.0.

";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::SetSeed "
";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::SetStandardDeviation "

Set/Get the standard deviation of the Gaussian distribution. Defaults
to 1.0.

";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AdditiveGaussianNoiseImageFilter::~AdditiveGaussianNoiseImageFilter "

Destructor

";


%feature("docstring") itk::simple::AffineTransform "

An affine transformation about a fixed center with translation for a
2D or 3D coordinate.



See:
 itk::AffineTransform


C++ includes: sitkAffineTransform.h
";

%feature("docstring")  itk::simple::AffineTransform::AffineTransform "
";

%feature("docstring")  itk::simple::AffineTransform::AffineTransform "
";

%feature("docstring")  itk::simple::AffineTransform::AffineTransform "
";

%feature("docstring")  itk::simple::AffineTransform::AffineTransform "
";

%feature("docstring")  itk::simple::AffineTransform::GetCenter "
";

%feature("docstring")  itk::simple::AffineTransform::GetMatrix "
";

%feature("docstring")  itk::simple::AffineTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AffineTransform::GetTranslation "

parameters

";

%feature("docstring")  itk::simple::AffineTransform::Rotate "
";

%feature("docstring")  itk::simple::AffineTransform::Scale "

additional methods

";

%feature("docstring")  itk::simple::AffineTransform::Scale "
";

%feature("docstring")  itk::simple::AffineTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::AffineTransform::SetMatrix "
";

%feature("docstring")  itk::simple::AffineTransform::SetTranslation "
";

%feature("docstring")  itk::simple::AffineTransform::Shear "
";

%feature("docstring")  itk::simple::AffineTransform::Translate "
";

%feature("docstring")  itk::simple::AffineTransform::~AffineTransform "
";


%feature("docstring") itk::simple::AggregateLabelMapFilter "

Collapses all labels into the first label.


This filter takes a label map as input and visits the pixels of all
labels and assigns them to the first label of the label map. At the
end of the execution of this filter, the map will contain a single
filter.

This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ShapeLabelObject , RelabelComponentImageFilter

 itk::simple::AggregateLabelMapFilter for the procedural interface

 itk::AggregateLabelMapFilter for the Doxygen on the original ITK class.


C++ includes: sitkAggregateLabelMapFilter.h
";

%feature("docstring")  itk::simple::AggregateLabelMapFilter::AggregateLabelMapFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AggregateLabelMapFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::AggregateLabelMapFilter::Execute "
";

%feature("docstring")  itk::simple::AggregateLabelMapFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AggregateLabelMapFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AggregateLabelMapFilter::~AggregateLabelMapFilter "

Destructor

";


%feature("docstring") itk::simple::AndImageFilter "

Implements the AND bitwise operator pixel-wise between two images.


This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.

Since the bitwise AND operation is only defined in C++ for integer
types, the images passed to this filter must comply with the
requirement of using integer pixel type.

The total operation over one pixel will be Where \"&\" is the bitwise AND operator in C++.
See:
 itk::simple::And for the procedural interface

 itk::AndImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAndImageFilter.h
";

%feature("docstring")  itk::simple::AndImageFilter::AndImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AndImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::AndImageFilter::Execute "
";

%feature("docstring")  itk::simple::AndImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::AndImageFilter::Execute "
";

%feature("docstring")  itk::simple::AndImageFilter::Execute "
";

%feature("docstring")  itk::simple::AndImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AndImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AndImageFilter::~AndImageFilter "

Destructor

";


%feature("docstring") itk::simple::AntiAliasBinaryImageFilter "

A method for estimation of a surface from a binary volume.



This filter implements a surface-fitting method for estimation of a
surface from a binary volume. This process can be used to reduce
aliasing artifacts which result in visualization of binary partitioned
surfaces.

The binary volume (filter input) is used as a set of constraints in an
iterative relaxation process of an estimated ND surface. The surface
is described implicitly as the zero level set of a volume $ \\\\phi $ and allowed to deform under curvature flow. A set of constraints is
imposed on this movement as follows:

\\\\[ u_{i,j,k}^{n+1} = \\\\left\\\\{ \\\\begin{array}{ll}
\\\\mbox{max} (u_{i,j,k}^{n} + \\\\Delta t H_{i,j,k}^{n}, 0) &
\\\\mbox{\\\\f$B_{i,j,k} = 1\\\\f$} \\\\\\\\ \\\\mbox{min}
(u_{i,j,k}^{n} + \\\\Delta t H_{i,j,k}^{n}, 0) &
\\\\mbox{\\\\f$B_{i,j,k} = -1\\\\f$} \\\\end{array}\\\\right. \\\\]

where $ u_{i,j,k}^{n} $ is the value of $ \\\\phi $ at discrete index $ (i,j,k) $ and iteration $ n $ , $ H $ is the gradient magnitude times mean curvature of $ \\\\phi $ , and $ B $ is the binary input volume, with 1 denoting an inside pixel and -1
denoting an outside pixel.
NOTES
This implementation uses a sparse field level set solver instead of
the narrow band implementation described in the reference below, which
may introduce some differences in how fast and how accurately (in
terms of RMS error) the solution converges.
REFERENCES
Whitaker, Ross. \"Reducing Aliasing Artifacts In Iso-Surfaces of
Binary Volumes\" IEEE Volume Visualization and Graphics Symposium,
October 2000, pp.23-32.
PARAMETERS
The MaximumRMSChange parameter is used to determine when the solution
has converged. A lower value will result in a tighter-fitting
solution, but will require more computations. Too low a value could
put the solver into an infinite loop. Values should always be less
than 1.0. A value of 0.07 is a good starting estimate.

The MaximumIterations parameter can be used to halt the solution after
a specified number of iterations.
INPUT
The input is an N-dimensional image of any type. It is assumed to be a
binary image. The filter will use an isosurface value that is halfway
between the min and max values in the image. A signed data type is not
necessary for the input.
OUTPUT
The filter will output a level set image of real, signed values. The
zero crossings of this (N-dimensional) image represent the position of
the isosurface value of interest. Values outside the zero level set
are negative and values inside the zero level set are positive values.
IMPORTANT!
The output image type you use to instantiate this filter should be a
real valued scalar type. In other words: doubles or floats.
USING THIS FILTER
The filter is relatively straightforward to use. Tests and examples
exist to illustrate. The important thing is to understand the input
and output types so you can properly interpret your results.

In the common case, the only parameter that will need to be set is the
MaximumRMSChange parameter, which determines when the solver halts.

See:
 itk::simple::AntiAliasBinary for the procedural interface

 itk::AntiAliasBinaryImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAntiAliasBinaryImageFilter.h
";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::AntiAliasBinaryImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::Execute "
";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::GetElapsedIterations "

Number of iterations run.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::GetRMSChange "

The Root Mean Square of the levelset upon termination.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AntiAliasBinaryImageFilter::~AntiAliasBinaryImageFilter "

Destructor

";


%feature("docstring") itk::simple::ApproximateSignedDistanceMapImageFilter "

Create a map of the approximate signed distance from the boundaries of
a binary image.


The ApproximateSignedDistanceMapImageFilter takes as input a binary image and produces a signed distance map.
Each pixel value in the output contains the approximate distance from
that pixel to the nearest \"object\" in the binary image. This filter
differs from the DanielssonDistanceMapImageFilter in that it calculates the distance to the \"object edge\" for pixels
within the object.

Negative values in the output indicate that the pixel at that position
is within an object in the input image. The absolute value of a
negative pixel represents the approximate distance to the nearest
object boundary pixel.

WARNING: This filter requires that the output type be floating-point.
Otherwise internal calculations will not be performed to the
appropriate precision, resulting in completely incorrect (read: zero-
valued) output.

The distances computed by this filter are Chamfer distances, which are
only an approximation to Euclidian distances, and are not as exact
approximations as those calculated by the DanielssonDistanceMapImageFilter . On the other hand, this filter is faster.

This filter requires that an \"inside value\" and \"outside value\" be
set as parameters. The \"inside value\" is the intensity value of the
binary image which corresponds to objects, and the \"outside value\"
is the intensity of the background. (A typical binary image often
represents objects as black (0) and background as white (usually 255),
or vice-versa.) Note that this filter is slightly faster if the inside
value is less than the outside value. Otherwise an extra iteration
through the image is required.

This filter uses the FastChamferDistanceImageFilter and the IsoContourDistanceImageFilter internally to perform the distance calculations.


See:
 DanielssonDistanceMapImageFilter

 SignedDanielssonDistanceMapImageFilter

 SignedMaurerDistanceMapImageFilter

 FastChamferDistanceImageFilter

 IsoContourDistanceImageFilter

Zach Pincus

See:
 itk::simple::ApproximateSignedDistanceMap for the procedural interface

 itk::ApproximateSignedDistanceMapImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkApproximateSignedDistanceMapImageFilter.h
";

%feature("docstring")  itk::simple::ApproximateSignedDistanceMapImageFilter::ApproximateSignedDistanceMapImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ApproximateSignedDistanceMapImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ApproximateSignedDistanceMapImageFilter::GetInsideValue "

Set/Get intensity value representing the interior of objects in the
mask.

";

%feature("docstring")  itk::simple::ApproximateSignedDistanceMapImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ApproximateSignedDistanceMapImageFilter::GetOutsideValue "

Set/Get intensity value representing the interior of objects in the
mask.

";

%feature("docstring")  itk::simple::ApproximateSignedDistanceMapImageFilter::SetInsideValue "

Set/Get intensity value representing the interior of objects in the
mask.

";

%feature("docstring")  itk::simple::ApproximateSignedDistanceMapImageFilter::SetOutsideValue "

Set/Get intensity value representing non-objects in the mask.

";

%feature("docstring")  itk::simple::ApproximateSignedDistanceMapImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ApproximateSignedDistanceMapImageFilter::~ApproximateSignedDistanceMapImageFilter "

Destructor

";


%feature("docstring") itk::simple::AsinImageFilter "

Computes the sine of each pixel.


This filter is templated over the pixel type of the input image and
the pixel type of the output image.

The filter walks over all the pixels in the input image, and for each
pixel does the following:


cast the pixel value to double ,

apply the std::asin() function to the double value,

cast the double value resulting from std::asin() to the pixel type of
the output image,

store the casted value into the output image.
 The filter expects both images to have the same dimension (e.g. both
2D, or both 3D, or both ND)
See:
 itk::simple::Asin for the procedural interface

 itk::AsinImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAsinImageFilter.h
";

%feature("docstring")  itk::simple::AsinImageFilter::AsinImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AsinImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::AsinImageFilter::Execute "
";

%feature("docstring")  itk::simple::AsinImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AsinImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AsinImageFilter::~AsinImageFilter "

Destructor

";


%feature("docstring") itk::simple::Atan2ImageFilter "

Computes two argument inverse tangent.


The first argument to the atan function is provided by a pixel in the
first input image (SetInput1() ) and the corresponding pixel in the
second input image (SetInput2() ) is used as the second argument.

This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.

Both pixel input types are cast to double in order to be used as
parameters of std::atan2() . The resulting double value is cast to the
output pixel type.
See:
 itk::simple::Atan2 for the procedural interface

 itk::Atan2ImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkAtan2ImageFilter.h
";

%feature("docstring")  itk::simple::Atan2ImageFilter::Atan2ImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::Atan2ImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::Atan2ImageFilter::Execute "
";

%feature("docstring")  itk::simple::Atan2ImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::Atan2ImageFilter::Execute "
";

%feature("docstring")  itk::simple::Atan2ImageFilter::Execute "
";

%feature("docstring")  itk::simple::Atan2ImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::Atan2ImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::Atan2ImageFilter::~Atan2ImageFilter "

Destructor

";


%feature("docstring") itk::simple::AtanImageFilter "

Computes the one-argument inverse tangent of each pixel.


This filter is templated over the pixel type of the input image and
the pixel type of the output image.

The filter walks over all the pixels in the input image, and for each
pixel does the following:


cast the pixel value to double ,

apply the std::atan() function to the double value,

cast the double value resulting from std::atan() to the pixel type of
the output image,

store the cast value into the output image.
See:
 itk::simple::Atan for the procedural interface

 itk::AtanImageFilter for the Doxygen on the original ITK class.



C++ includes: sitkAtanImageFilter.h
";

%feature("docstring")  itk::simple::AtanImageFilter::AtanImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::AtanImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::AtanImageFilter::Execute "
";

%feature("docstring")  itk::simple::AtanImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::AtanImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::AtanImageFilter::~AtanImageFilter "

Destructor

";


%feature("docstring") itk::simple::BSplineDecompositionImageFilter "

Calculates the B-Spline coefficients of an image. Spline order may be
from 0 to 5.


This class defines N-Dimension B-Spline transformation. It is based
on: [1] M. Unser, \"Splines: A Perfect Fit for Signal and Image
Processing,\" IEEE Signal Processing Magazine, vol. 16, no. 6, pp.
22-38, November 1999. [2] M. Unser, A. Aldroubi and M. Eden,
\"B-Spline Signal Processing: Part I--Theory,\" IEEE Transactions on
Signal Processing, vol. 41, no. 2, pp. 821-832, February 1993. [3] M.
Unser, A. Aldroubi and M. Eden, \"B-Spline Signal Processing: Part II
--Efficient Design and Applications,\" IEEE Transactions on Signal
Processing, vol. 41, no. 2, pp. 834-848, February 1993. And code obtained from bigwww.epfl.ch by Philippe Thevenaz

Limitations: Spline order must be between 0 and 5. Spline order must
be set before setting the image. Uses mirror boundary conditions.
Requires the same order of Spline for each dimension. Can only process
LargestPossibleRegion


See:
 BSplineResampleImageFunction

 itk::simple::BSplineDecomposition for the procedural interface

 itk::BSplineDecompositionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBSplineDecompositionImageFilter.h
";

%feature("docstring")  itk::simple::BSplineDecompositionImageFilter::BSplineDecompositionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BSplineDecompositionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BSplineDecompositionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BSplineDecompositionImageFilter::GetSplineOrder "
";

%feature("docstring")  itk::simple::BSplineDecompositionImageFilter::GetSplinePoles "

Get the poles calculated for a given spline order.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::BSplineDecompositionImageFilter::SetSplineOrder "

Get/Sets the Spline Order, supports 0th - 5th order splines. The
default is a 3rd order spline.

";

%feature("docstring")  itk::simple::BSplineDecompositionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BSplineDecompositionImageFilter::~BSplineDecompositionImageFilter "

Destructor

";


%feature("docstring") itk::simple::BSplineTransform "

A deformable transform over a bounded spatial domain using a BSpline
representation for a 2D or 3D coordinate space.



See:
 itk::BSplineTransform


C++ includes: sitkBSplineTransform.h
";

%feature("docstring")  itk::simple::BSplineTransform::BSplineTransform "
";

%feature("docstring")  itk::simple::BSplineTransform::BSplineTransform "

Construct a BSpline from a set of coeefficientImages

The coefficient images must be of pixel type sitkFloat64, the number
of images must equal the dimension of the transform ( 2 or 3 ), all
must be the same dimensions. The image's spacing, origin, size and
direction cosine matrix are used to define the transform domain. The
transform domain is reduced by the spline order.

";

%feature("docstring")  itk::simple::BSplineTransform::BSplineTransform "
";

%feature("docstring")  itk::simple::BSplineTransform::BSplineTransform "
";

%feature("docstring")  itk::simple::BSplineTransform::GetCoefficientImages "

Get a vector of the coefficient images representing the BSpline.


A lazy shallow copy of the images from ITK is performed. If they are
modified in SimpleITK a deep copy will occur. However, if the
coefficient images are modified in ITK, then no copy will occur and
the images held by SimpleITK may unexpectedly change.

";

%feature("docstring")  itk::simple::BSplineTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BSplineTransform::GetOrder "
";

%feature("docstring")  itk::simple::BSplineTransform::GetTransformDomainDirection "
";

%feature("docstring")  itk::simple::BSplineTransform::GetTransformDomainMeshSize "
";

%feature("docstring")  itk::simple::BSplineTransform::GetTransformDomainOrigin "
";

%feature("docstring")  itk::simple::BSplineTransform::GetTransformDomainPhysicalDimensions "
";

%feature("docstring")  itk::simple::BSplineTransform::SetTransformDomainDirection "

parameters fixed parameter

";

%feature("docstring")  itk::simple::BSplineTransform::SetTransformDomainMeshSize "
";

%feature("docstring")  itk::simple::BSplineTransform::SetTransformDomainOrigin "
";

%feature("docstring")  itk::simple::BSplineTransform::SetTransformDomainPhysicalDimensions "
";

%feature("docstring")  itk::simple::BSplineTransform::~BSplineTransform "
";


%feature("docstring") itk::simple::BSplineTransformInitializerFilter "

BSplineTransformInitializerFilter is a helper class intended to initialize the control point grid such
that it has a physically consistent definition. It sets the transform
domain origin, physical dimensions and direction from information
obtained from the image. It also sets the mesh size if asked to do so
by calling SetTransformDomainMeshSize()before calling InitializeTransform().



Luis Ibanez Nick Tustison

See:
 itk::simple::BSplineTransformInitializer for the procedural interface

 itk::BSplineTransformInitializer for the Doxygen on the original ITK class.


C++ includes: sitkBSplineTransformInitializerFilter.h
";

%feature("docstring")  itk::simple::BSplineTransformInitializerFilter::BSplineTransformInitializerFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BSplineTransformInitializerFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BSplineTransformInitializerFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BSplineTransformInitializerFilter::GetOrder "
";

%feature("docstring")  itk::simple::BSplineTransformInitializerFilter::GetTransformDomainMeshSize "
";

%feature("docstring")  itk::simple::BSplineTransformInitializerFilter::SetOrder "

The order of the bspline in the output BSplineTransform. This value effects the number of control points.

";

%feature("docstring")  itk::simple::BSplineTransformInitializerFilter::SetTransformDomainMeshSize "

Allow the user to set the mesh size of the transform via the
initializer even though the initializer does not do anything with that
information. Defeault = 1^ImageDimension.

";

%feature("docstring")  itk::simple::BSplineTransformInitializerFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BSplineTransformInitializerFilter::~BSplineTransformInitializerFilter "

Destructor

";


%feature("docstring") itk::simple::BilateralImageFilter "

Blurs an image while preserving edges.


This filter uses bilateral filtering to blur an image using both
domain and range \"neighborhoods\". Pixels that are close to a pixel
in the image domain and similar to a pixel in the image range are used
to calculate the filtered value. Two gaussian kernels (one in the
image domain and one in the image range) are used to smooth the image.
The result is an image that is smoothed in homogeneous regions yet has
edges preserved. The result is similar to anisotropic diffusion but
the implementation in non-iterative. Another benefit to bilateral
filtering is that any distance metric can be used for kernel smoothing
the image range. Hence, color images can be smoothed as vector images,
using the CIE distances between intensity values as the similarity
metric (the Gaussian kernel for the image domain is evaluated using
CIE distances). A separate version of this filter will be designed for
color and vector images.

Bilateral filtering is capable of reducing the noise in an image by an
order of magnitude while maintaining edges.

The bilateral operator used here was described by Tomasi and Manduchi
(Bilateral Filtering for Gray and ColorImages. IEEE ICCV. 1998.)


See:
 GaussianOperator

 RecursiveGaussianImageFilter

 DiscreteGaussianImageFilter

 AnisotropicDiffusionImageFilter

 Image

 Neighborhood

 NeighborhoodOperator
 TodoSupport color images

Support vector images
See:
 itk::simple::Bilateral for the procedural interface

 itk::BilateralImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBilateralImageFilter.h
";

%feature("docstring")  itk::simple::BilateralImageFilter::BilateralImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BilateralImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BilateralImageFilter::GetDomainSigma "

Standard get/set macros for filter parameters. DomainSigma is
specified in the same units as the Image spacing. RangeSigma is specified in the units of intensity.

";

%feature("docstring")  itk::simple::BilateralImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BilateralImageFilter::GetNumberOfRangeGaussianSamples "

Set/Get the number of samples in the approximation to the Gaussian
used for the range smoothing. Samples are only generated in the range
of [0, 4*m_RangeSigma]. Default is 100.

";

%feature("docstring")  itk::simple::BilateralImageFilter::GetRangeSigma "

Standard get/set macros for filter parameters. DomainSigma is
specified in the same units as the Image spacing. RangeSigma is specified in the units of intensity.

";

%feature("docstring")  itk::simple::BilateralImageFilter::SetDomainSigma "

Convenience get/set methods for setting all domain parameters to the
same values.

";

%feature("docstring")  itk::simple::BilateralImageFilter::SetNumberOfRangeGaussianSamples "

Set/Get the number of samples in the approximation to the Gaussian
used for the range smoothing. Samples are only generated in the range
of [0, 4*m_RangeSigma]. Default is 100.

";

%feature("docstring")  itk::simple::BilateralImageFilter::SetRangeSigma "

Standard get/set macros for filter parameters. DomainSigma is
specified in the same units as the Image spacing. RangeSigma is specified in the units of intensity.

";

%feature("docstring")  itk::simple::BilateralImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BilateralImageFilter::~BilateralImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinShrinkImageFilter "

Reduce the size of an image by an integer factor in each dimension
while performing averaging of an input neighborhood.


The output image size in each dimension is given by:

outputSize[j] = max( std::floor(inputSize[j]/shrinkFactor[j]), 1 );

The algorithm implemented can be describe with the following equation
for 2D: \\\\[ \\\\mathsf{I}_{out}(x_o,x_1) =
\\\\frac{\\\\sum_{i=0}^{f_0}\\\\sum_{j=0}^{f_1}\\\\mathsf{I}_{in}(f_0
x_o+i,f_1 x_1+j)}{f_0 f_1} \\\\]

This filter is implemented so that the starting extent of the first
pixel of the output matches that of the input.

The change in image geometry from a 5x5 image binned by a factor of
2x2.

This code was contributed in the Insight Journal paper: \"BinShrink: A
multi-resolution filter with cache efficient averaging\" by Lowekamp
B., Chen D. https://hdl.handle.net/10380/3450
See:
 itk::simple::BinShrink for the procedural interface

 itk::BinShrinkImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinShrinkImageFilter.h
";

%feature("docstring")  itk::simple::BinShrinkImageFilter::BinShrinkImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinShrinkImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinShrinkImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinShrinkImageFilter::GetShrinkFactors "

Get the shrink factors.

";

%feature("docstring")  itk::simple::BinShrinkImageFilter::SetShrinkFactor "

Custom public declarations

";

%feature("docstring")  itk::simple::BinShrinkImageFilter::SetShrinkFactors "

Set the shrink factors. Values are clamped to a minimum value of 1.
Default is 1 for all dimensions.

";

%feature("docstring")  itk::simple::BinShrinkImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinShrinkImageFilter::~BinShrinkImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryClosingByReconstructionImageFilter "

binary closing by reconstruction of an image.


This filter removes small (i.e., smaller than the structuring element)
holes in the image. It is defined as: Closing(f) =
ReconstructionByErosion(Dilation(f)).

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 MorphologyImageFilter , ClosingByReconstructionImageFilter , BinaryOpeningByReconstructionImageFilter

 itk::simple::BinaryClosingByReconstruction for the procedural interface

 itk::BinaryClosingByReconstructionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryClosingByReconstructionImageFilter.h
";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::BinaryClosingByReconstructionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::GetForegroundValue "

Get the value in the image considered as \"foreground\". Defaults to
maximum value of InputPixelType.

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::SetForegroundValue "

Set the value in the image to consider as \"foreground\". Defaults to
maximum value of InputPixelType.

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryClosingByReconstructionImageFilter::~BinaryClosingByReconstructionImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryContourImageFilter "

Labels the pixels on the border of the objects in a binary image.


BinaryContourImageFilter takes a binary image as input, where the pixels in the objects are
the pixels with a value equal to ForegroundValue. Only the pixels on
the contours of the objects are kept. The pixels not on the border are
changed to BackgroundValue.

The connectivity can be changed to minimum or maximum connectivity
with SetFullyConnected() . Full connectivity produces thicker contours.

https://hdl.handle.net/1926/1352


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 LabelContourImageFilter BinaryErodeImageFilter SimpleContourExtractorImageFilter

 itk::simple::BinaryContour for the procedural interface

 itk::BinaryContourImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryContourImageFilter.h
";

%feature("docstring")  itk::simple::BinaryContourImageFilter::BinaryContourImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::Execute "
";

%feature("docstring")  itk::simple::BinaryContourImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::BinaryContourImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::GetBackgroundValue "

Set/Get the background value used to mark the pixels not on the border
of the objects.

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::GetForegroundValue "

Set/Get the foreground value used to identify the objects in the input
and output images.

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::SetBackgroundValue "

Set/Get the background value used to mark the pixels not on the border
of the objects.

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::SetForegroundValue "

Set/Get the foreground value used to identify the objects in the input
and output images.

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryContourImageFilter::~BinaryContourImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryDilateImageFilter "

Fast binary dilation of a single intensity value in the image.


BinaryDilateImageFilter is a binary dilation morphologic operation on the foreground of an
image. Only the value designated by the intensity value
\"SetForegroundValue()\" (alias as SetDilateValue() ) is considered as
foreground, and other intensity values are considered background.

Gray scale images can be processed as binary images by selecting a
\"ForegroundValue\" (alias \"DilateValue\"). Pixel values matching the
dilate value are considered the \"foreground\" and all other pixels
are \"background\". This is useful in processing segmented images
where all pixels in segment #1 have value 1 and pixels in segment #2
have value 2, etc. A particular \"segment number\" can be processed.
ForegroundValue defaults to the maximum possible value of the
PixelType.

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel. A reasonable choice
of structuring element is itk::BinaryBallStructuringElement .

This implementation is based on the papers:

L.Vincent \"Morphological transformations of binary images with
arbitrary structuring elements\", and

N.Nikopoulos et al. \"An efficient algorithm for 3d binary
morphological transformations with 3d structuring elements for
arbitrary size and shape\". IEEE Transactions on Image Processing. Vol. 9. No. 3. 2000. pp. 283-286.


See:
 ImageToImageFilter BinaryErodeImageFilter BinaryMorphologyImageFilter

 itk::simple::BinaryDilate for the procedural interface

 itk::BinaryDilateImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryDilateImageFilter.h
";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::BinaryDilateImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::BoundaryToForegroundOff "
";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::BoundaryToForegroundOn "

Set the value of BoundaryToForeground to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::GetBackgroundValue "
";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::GetBoundaryToForeground "
";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::GetForegroundValue "
";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::SetBackgroundValue "
";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::SetBoundaryToForeground "
";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::SetForegroundValue "
";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryDilateImageFilter::~BinaryDilateImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryErodeImageFilter "

Fast binary erosion of a single intensity value in the image.


BinaryErodeImageFilter is a binary erosion morphologic operation on the foreground of an
image. Only the value designated by the intensity value
\"SetForegroundValue()\" (alias as SetErodeValue() ) is considered as
foreground, and other intensity values are considered background.

Gray scale images can be processed as binary images by selecting a
\"ForegroundValue\" (alias \"ErodeValue\"). Pixel values matching the
erode value are considered the \"foreground\" and all other pixels are
\"background\". This is useful in processing segmented images where
all pixels in segment #1 have value 1 and pixels in segment #2 have
value 2, etc. A particular \"segment number\" can be processed.
ForegroundValue defaults to the maximum possible value of the
PixelType. The eroded pixels will receive the BackgroundValue
(defaults to NumericTraits::NonpositiveMin() ).

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel. A reasonable choice
of structuring element is itk::BinaryBallStructuringElement .

This implementation is based on the papers:

L.Vincent \"Morphological transformations of binary images with
arbitrary structuring elements\", and

N.Nikopoulos et al. \"An efficient algorithm for 3d binary
morphological transformations with 3d structuring elements for
arbitrary size and shape\". IEEE Transactions on Image Processing. Vol. 9. No. 3. 2000. pp. 283-286.


See:
 ImageToImageFilter BinaryDilateImageFilter BinaryMorphologyImageFilter

 itk::simple::BinaryErode for the procedural interface

 itk::BinaryErodeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryErodeImageFilter.h
";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::BinaryErodeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::BoundaryToForegroundOff "
";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::BoundaryToForegroundOn "

Set the value of BoundaryToForeground to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::GetBackgroundValue "
";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::GetBoundaryToForeground "
";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::GetForegroundValue "
";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::SetBackgroundValue "
";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::SetBoundaryToForeground "
";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::SetForegroundValue "
";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryErodeImageFilter::~BinaryErodeImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryFillholeImageFilter "

Remove holes not connected to the boundary of the image.


BinaryFillholeImageFilter fills holes in a binary image.

Geodesic morphology and the Fillhole algorithm is described in Chapter
6 of Pierre Soille's book \"Morphological Image Analysis:  Principles
and Applications\", Second Edition, Springer, 2003.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 GrayscaleFillholeImageFilter

 itk::simple::BinaryFillhole for the procedural interface

 itk::BinaryFillholeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryFillholeImageFilter.h
";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::BinaryFillholeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::GetForegroundValue "

Get the value in the image considered as \"foreground\". Defaults to
maximum value of InputPixelType.

";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::SetForegroundValue "

Set the value in the image to consider as \"foreground\". Defaults to
maximum value of InputPixelType.

";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryFillholeImageFilter::~BinaryFillholeImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryGrindPeakImageFilter "

Remove the objects not connected to the boundary of the image.


BinaryGrindPeakImageFilter ginds peaks in a grayscale image.

Geodesic morphology and the grind peak algorithm is described in
Chapter 6 of Pierre Soille's book \"Morphological Image Analysis:
Principles and Applications\", Second Edition, Springer, 2003.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 GrayscaleGrindPeakImageFilter

 itk::simple::BinaryGrindPeak for the procedural interface

 itk::BinaryGrindPeakImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryGrindPeakImageFilter.h
";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::BinaryGrindPeakImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::GetBackgroundValue "

Set the value in eroded part of the image. Defaults to zero

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::GetForegroundValue "

Get the value in the image considered as \"foreground\". Defaults to
maximum value of InputPixelType.

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::SetBackgroundValue "

Set the value in eroded part of the image. Defaults to zero

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::SetForegroundValue "

Set the value in the image to consider as \"foreground\". Defaults to
maximum value of InputPixelType.

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryGrindPeakImageFilter::~BinaryGrindPeakImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryImageToLabelMapFilter "

Label the connected components in a binary image and produce a collection
of label objects.


BinaryImageToLabelMapFilter labels the objects in a binary image. Each distinct object is
assigned a unique label. The final object labels start with 1 and are
consecutive. Objects that are reached earlier by a raster order scan
have a lower label.

The GetOutput() function of this class returns an itk::LabelMap .

This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ConnectedComponentImageFilter , LabelImageToLabelMapFilter , LabelMap , LabelObject

 itk::simple::BinaryImageToLabelMapFilter for the procedural interface

 itk::BinaryImageToLabelMapFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryImageToLabelMapFilter.h
";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::BinaryImageToLabelMapFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::GetInputForegroundValue "

Set/Get the value to be consider \"foreground\" in the input image.
Defaults to NumericTraits<InputPixelType>::max() .

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::GetNumberOfObjects "

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::GetOutputBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<OutputPixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::SetInputForegroundValue "

Set/Get the value to be consider \"foreground\" in the input image.
Defaults to NumericTraits<InputPixelType>::max() .

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::SetOutputBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<OutputPixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryImageToLabelMapFilter::~BinaryImageToLabelMapFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryMagnitudeImageFilter "

Computes the square root of the sum of squares of corresponding input
pixels.


This filter is templated over the types of the two input images and
the type of the output image.

Numeric conversions (castings) are done by the C++ defaults.

The filter walks over all of the pixels in the two input images, and
for each pixel does the following:


cast the input 1 pixel value to double

cast the input 2 pixel value to double

compute the sum of squares of the two pixel values

compute the square root of the sum

cast the double value resulting from std::sqrt() to the pixel type of
the output image

store the cast value into the output image.
 The filter expects all images to have the same dimension (e.g. all
2D, or all 3D, or all ND)
See:
 itk::simple::BinaryMagnitude for the procedural interface

 itk::BinaryMagnitudeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryMagnitudeImageFilter.h
";

%feature("docstring")  itk::simple::BinaryMagnitudeImageFilter::BinaryMagnitudeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryMagnitudeImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::BinaryMagnitudeImageFilter::Execute "
";

%feature("docstring")  itk::simple::BinaryMagnitudeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryMagnitudeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryMagnitudeImageFilter::~BinaryMagnitudeImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryMedianImageFilter "

Applies a version of the median filter optimized for binary images.


This filter was contributed by Bjorn Hanch Sollie after identifying
that the generic Median filter performed unnecessary operations when
the input image is binary.

This filter computes an image where a given pixel is the median value
of the pixels in a neighborhood about the corresponding input pixel.
For the case of binary images the median can be obtained by simply
counting the neighbors that are foreground.

A median filter is one of the family of nonlinear filters. It is used
to smooth an image without being biased by outliers or shot noise.


See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::BinaryMedian for the procedural interface

 itk::BinaryMedianImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryMedianImageFilter.h
";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::BinaryMedianImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::GetBackgroundValue "

Get the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::GetForegroundValue "

Get the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::GetRadius "

Get the radius of the neighborhood used to compute the median

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::SetBackgroundValue "

Set the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::SetForegroundValue "

Set the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::SetRadius "

Set the radius of the neighborhood used to compute the median.

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryMedianImageFilter::~BinaryMedianImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryMinMaxCurvatureFlowImageFilter "

Denoise a binary image using min/max curvature flow.


BinaryMinMaxCurvatureFlowImageFilter implements a curvature driven image denoising algorithm. This filter
assumes that the image is essentially binary: consisting of two
classes. Iso-brightness contours in the input image are viewed as a
level set. The level set is then evolved using a curvature-based speed
function:

\\\\[ I_t = F_{\\\\mbox{minmax}} |\\\\nabla I| \\\\]

where $ F_{\\\\mbox{minmax}} = \\\\min(\\\\kappa,0) $ if $ \\\\mbox{Avg}_{\\\\mbox{stencil}}(x) $ is less than or equal to $ T_{threshold} $ and $ \\\\max(\\\\kappa,0) $ , otherwise. $ \\\\kappa $ is the mean curvature of the iso-brightness contour at point $ x $ .

In min/max curvature flow, movement is turned on or off depending on
the scale of the noise one wants to remove. Switching depends on the
average image value of a region of radius $ R $ around each point. The choice of $ R $ , the stencil radius, governs the scale of the noise to be removed.

The threshold value $ T_{threshold} $ is a user specified value which discriminates between the two pixel
classes.

This filter make use of the multi-threaded finite difference solver
hierarchy. Updates are computed using a BinaryMinMaxCurvatureFlowFunction object. A zero flux Neumann boundary condition is used when computing
derivatives near the data boundary.


WARNING:
This filter assumes that the input and output types have the same
dimensions. This filter also requires that the output image pixels are
of a real type. This filter works for any dimensional images.
 Reference: \"Level Set Methods and Fast Marching Methods\", J.A.
Sethian, Cambridge Press, Chapter 16, Second edition, 1999.


See:
 BinaryMinMaxCurvatureFlowFunction

 CurvatureFlowImageFilter

 MinMaxCurvatureFlowImageFilter

 itk::simple::BinaryMinMaxCurvatureFlow for the procedural interface

 itk::BinaryMinMaxCurvatureFlowImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryMinMaxCurvatureFlowImageFilter.h
";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::BinaryMinMaxCurvatureFlowImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::Execute "
";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::GetStencilRadius "
";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::GetThreshold "

Set/Get the threshold value.

";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::GetTimeStep "
";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::SetStencilRadius "
";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::SetThreshold "

Set/Get the threshold value.

";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::SetTimeStep "
";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryMinMaxCurvatureFlowImageFilter::~BinaryMinMaxCurvatureFlowImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryMorphologicalClosingImageFilter "

binary morphological closing of an image.


This filter removes small (i.e., smaller than the structuring element)
holes and tube like structures in the interior or at the boundaries of
the image. The morphological closing of an image \"f\" is defined as:
Closing(f) = Erosion(Dilation(f)).

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.

This code was contributed in the Insight Journal paper: \"Binary
morphological closing and opening image filters\" by Lehmann G. https://hdl.handle.net/1926/141 http://www.insight-journal.org/browse/publication/58


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleErodeImageFilter

 itk::simple::BinaryMorphologicalClosing for the procedural interface

 itk::BinaryMorphologicalClosingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryMorphologicalClosingImageFilter.h
";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::BinaryMorphologicalClosingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::GetForegroundValue "

Get the value in the image considered as \"foreground\". Defaults to
maximum value of InputPixelType.

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::GetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::SafeBorderOff "
";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::SafeBorderOn "

Set the value of SafeBorder to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::SetForegroundValue "

Set the value in the image to consider as \"foreground\". Defaults to
maximum value of InputPixelType.

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::SetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryMorphologicalClosingImageFilter::~BinaryMorphologicalClosingImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryMorphologicalOpeningImageFilter "

binary morphological opening of an image.


This filter removes small (i.e., smaller than the structuring element)
structures in the interior or at the boundaries of the image. The
morphological opening of an image \"f\" is defined as: Opening(f) =
Dilatation(Erosion(f)).

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.

This code was contributed in the Insight Journal paper: \"Binary
morphological closing and opening image filters\" by Lehmann G. https://hdl.handle.net/1926/141 http://www.insight-journal.org/browse/publication/58


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleErodeImageFilter

 itk::simple::BinaryMorphologicalOpening for the procedural interface

 itk::BinaryMorphologicalOpeningImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryMorphologicalOpeningImageFilter.h
";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::BinaryMorphologicalOpeningImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::GetBackgroundValue "

Set the value in eroded part of the image. Defaults to zero

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::GetForegroundValue "

Get the value in the image considered as \"foreground\". Defaults to
maximum value of PixelType.

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::SetBackgroundValue "

Set the value in eroded part of the image. Defaults to zero

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::SetForegroundValue "

Set the value in the image to consider as \"foreground\". Defaults to
maximum value of PixelType.

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryMorphologicalOpeningImageFilter::~BinaryMorphologicalOpeningImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryNotImageFilter "

Implements the BinaryNot logical operator pixel-wise between two
images.


This class is parameterized over the types of the two input images and
the type of the output image. Numeric conversions (castings) are done
by the C++ defaults.

The total operation over one pixel will be

output_pixel = static_cast<PixelType>( input1_pixel != input2_pixel )

Where \"!=\" is the equality operator in C++.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176
See:
 itk::simple::BinaryNot for the procedural interface

 itk::BinaryNotImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryNotImageFilter.h
";

%feature("docstring")  itk::simple::BinaryNotImageFilter::BinaryNotImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryNotImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryNotImageFilter::Execute "
";

%feature("docstring")  itk::simple::BinaryNotImageFilter::GetBackgroundValue "

Get the value used as \"background\". Defaults to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryNotImageFilter::GetForegroundValue "

Set/Get the value in the image considered as \"foreground\". Defaults
to maximum value of PixelType.

";

%feature("docstring")  itk::simple::BinaryNotImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryNotImageFilter::SetBackgroundValue "

Set the value used as \"background\". Defaults to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryNotImageFilter::SetForegroundValue "

Set/Get the value in the image considered as \"foreground\". Defaults
to maximum value of PixelType.

";

%feature("docstring")  itk::simple::BinaryNotImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryNotImageFilter::~BinaryNotImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryOpeningByReconstructionImageFilter "

binary morphological closing of an image.


This filter removes small (i.e., smaller than the structuring element)
objects in the image. It is defined as: Opening(f) =
ReconstructionByDilatation(Erosion(f)).

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 MorphologyImageFilter , OpeningByReconstructionImageFilter , BinaryClosingByReconstructionImageFilter

 itk::simple::BinaryOpeningByReconstruction for the procedural interface

 itk::BinaryOpeningByReconstructionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryOpeningByReconstructionImageFilter.h
";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::BinaryOpeningByReconstructionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::GetBackgroundValue "

Set the value in eroded part of the image. Defaults to zero

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::GetForegroundValue "

Get the value in the image considered as \"foreground\". Defaults to
maximum value of PixelType.

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::SetBackgroundValue "

Set the value in eroded part of the image. Defaults to zero

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::SetForegroundValue "

Set the value in the image to consider as \"foreground\". Defaults to
maximum value of PixelType.

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryOpeningByReconstructionImageFilter::~BinaryOpeningByReconstructionImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryProjectionImageFilter "

Binary projection.


This class was contributed to the Insight Journal by Gaetan Lehmann.
The original paper can be found at https://hdl.handle.net/1926/164


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ProjectionImageFilter

 MedianProjectionImageFilter

 MeanProjectionImageFilter

 MeanProjectionImageFilter

 MaximumProjectionImageFilter

 MinimumProjectionImageFilter

 StandardDeviationProjectionImageFilter

 SumProjectionImageFilter

 itk::simple::BinaryProjection for the procedural interface

 itk::BinaryProjectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryProjectionImageFilter.h
";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::BinaryProjectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::GetBackgroundValue "

Get the value used as \"background\". Any pixel value which is not
DilateValue is considered background. BackgroundValue is used for
defining boundary conditions. Defaults to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::GetForegroundValue "

Get the value in the image considered as \"foreground\". Defaults to
maximum value of PixelType.

";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::GetProjectionDimension "
";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::SetBackgroundValue "

Set the value used as \"background\". Any pixel value which is not
DilateValue is considered background. BackgroundValue is used for
defining boundary conditions. Defaults to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::SetForegroundValue "

Set the value in the image to consider as \"foreground\". Defaults to
maximum value of PixelType. Subclasses may alias this to DilateValue
or ErodeValue.

";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::SetProjectionDimension "
";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryProjectionImageFilter::~BinaryProjectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryPruningImageFilter "

This filter removes \"spurs\" of less than a certain length in the
input image.


This class is parameterized over the type of the input image and the
type of the output image.

The input is assumed to be a binary image.

This filter is a sequential pruning algorithm and known to be
computational time dependable of the image size. The algorithm is the
N-dimensional version of that given for two dimensions in:

Rafael C. Gonzales and Richard E. Woods. Digital Image Processing. Addison Wesley, 491-494, (1993).


See:
 MorphologyImageFilter

 BinaryErodeImageFilter

 BinaryDilateImageFilter

 BinaryThinningImageFilter

 itk::simple::BinaryPruning for the procedural interface

 itk::BinaryPruningImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryPruningImageFilter.h
";

%feature("docstring")  itk::simple::BinaryPruningImageFilter::BinaryPruningImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryPruningImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryPruningImageFilter::GetIteration "

Set/Get the iteration value

";

%feature("docstring")  itk::simple::BinaryPruningImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryPruningImageFilter::SetIteration "

Set/Get the iteration value

";

%feature("docstring")  itk::simple::BinaryPruningImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryPruningImageFilter::~BinaryPruningImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryReconstructionByDilationImageFilter "

binary reconstruction by dilation of an image


Reconstruction by dilation operates on a \"marker\" image and a
\"mask\" image, and is defined as the dilation of the marker image
with respect to the mask image iterated until stability.

Geodesic morphology is described in Chapter 6.2 of Pierre Soille's
book \"Morphological Image Analysis: Principles and Applications\",
Second Edition, Springer, 2003.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 MorphologyImageFilter , ReconstructionByDilationImageFilter , BinaryReconstructionByErosionImageFilter

 itk::simple::BinaryReconstructionByDilation for the procedural interface

 itk::BinaryReconstructionByDilationImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryReconstructionByDilationImageFilter.h
";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::BinaryReconstructionByDilationImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::GetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::GetForegroundValue "

Set/Get the value used as \"foreground\" in the output image. Defaults
to NumericTraits<PixelType>::max() .

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::SetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::SetForegroundValue "

Set/Get the value used as \"foreground\" in the output image. Defaults
to NumericTraits<PixelType>::max() .

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryReconstructionByDilationImageFilter::~BinaryReconstructionByDilationImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryReconstructionByErosionImageFilter "

binary reconstruction by erosion of an image


Reconstruction by erosion operates on a \"marker\" image and a
\"mask\" image, and is defined as the erosion of the marker image with
respect to the mask image iterated until stability.

Geodesic morphology is described in Chapter 6.2 of Pierre Soille's
book \"Morphological Image Analysis: Principles and Applications\",
Second Edition, Springer, 2003.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 MorphologyImageFilter , ReconstructionByErosionImageFilter , BinaryReconstructionByDilationImageFilter

 itk::simple::BinaryReconstructionByErosion for the procedural interface

 itk::BinaryReconstructionByErosionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryReconstructionByErosionImageFilter.h
";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::BinaryReconstructionByErosionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::GetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::GetForegroundValue "

Set/Get the value used as \"foreground\" in the output image. Defaults
to NumericTraits<PixelType>::max() .

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::SetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::SetForegroundValue "

Set/Get the value used as \"foreground\" in the output image. Defaults
to NumericTraits<PixelType>::max() .

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryReconstructionByErosionImageFilter::~BinaryReconstructionByErosionImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryThinningImageFilter "

This filter computes one-pixel-wide edges of the input image.


This class is parameterized over the type of the input image and the
type of the output image.

The input is assumed to be a binary image. If the foreground pixels of
the input image do not have a value of 1, they are rescaled to 1
internally to simplify the computation.

The filter will produce a skeleton of the object. The output
background values are 0, and the foreground values are 1.

This filter is a sequential thinning algorithm and known to be
computational time dependable on the image size. The algorithm
corresponds with the 2D implementation described in:

Rafael C. Gonzales and Richard E. Woods. Digital Image Processing. Addison Wesley, 491-494, (1993).

To do: Make this filter ND.


See:
 MorphologyImageFilter

 itk::simple::BinaryThinning for the procedural interface

 itk::BinaryThinningImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryThinningImageFilter.h
";

%feature("docstring")  itk::simple::BinaryThinningImageFilter::BinaryThinningImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryThinningImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryThinningImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryThinningImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryThinningImageFilter::~BinaryThinningImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryThresholdImageFilter "

Binarize an input image by thresholding.


This filter produces an output image whose pixels are either one of
two values ( OutsideValue or InsideValue ), depending on whether the
corresponding input image pixels lie between the two thresholds (
LowerThreshold and UpperThreshold ). Values equal to either threshold
is considered to be between the thresholds.

More precisely \\\\[ Output(x_i) = \\\\begin{cases} InsideValue & \\\\text{if
\\\\f$LowerThreshold \\\\leq x_i \\\\leq UpperThreshold\\\\f$}
\\\\\\\\ OutsideValue & \\\\text{otherwise} \\\\end{cases} \\\\]

This filter is templated over the input image type and the output
image type.

The filter expect both images to have the same number of dimensions.

The default values for LowerThreshold and UpperThreshold are:
LowerThreshold = NumericTraits<TInput>::NonpositiveMin() ; UpperThreshold = NumericTraits<TInput>::max() ; Therefore, generally only one of these needs to be set, depending
on whether the user wants to threshold above or below the desired
threshold.
See:
 itk::simple::BinaryThreshold for the procedural interface

 itk::BinaryThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryThresholdImageFilter.h
";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::BinaryThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::GetLowerThreshold "
";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::GetUpperThreshold "

Get the threshold values.

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value. The default value NumericTraits<OutputPixelType>::max()

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::SetLowerThreshold "
";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::ZeroValue() .

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::SetUpperThreshold "

Set the thresholds. The default lower threshold is NumericTraits<InputPixelType>::NonpositiveMin() . The default upper threshold is NumericTraits<InputPixelType>::max . An exception is thrown if the lower threshold is greater than the
upper threshold.

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryThresholdImageFilter::~BinaryThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinaryThresholdProjectionImageFilter "

BinaryThreshold projection.


This class was contributed to the Insight Journal by Gaetan Lehmann.
the original paper can be found at https://hdl.handle.net/1926/164


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ProjectionImageFilter

 MedianProjectionImageFilter

 MeanProjectionImageFilter

 MeanProjectionImageFilter

 MaximumProjectionImageFilter

 MinimumProjectionImageFilter

 StandardDeviationProjectionImageFilter

 SumProjectionImageFilter

 itk::simple::BinaryThresholdProjection for the procedural interface

 itk::BinaryThresholdProjectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinaryThresholdProjectionImageFilter.h
";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::BinaryThresholdProjectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::GetBackgroundValue "

Set/Get the output value used as \"background\". Defaults to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::GetForegroundValue "

Set/Get the output value used as \"foreground\". Defaults to maximum
value of PixelType.

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::GetProjectionDimension "
";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::GetThresholdValue "

Set/Get the input value consider as \"threshold\". Defaults to NumericTraits<InputPixelType>::max()

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::SetBackgroundValue "

Set/Get the output value used as \"background\". Defaults to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::SetForegroundValue "

Set/Get the output value used as \"foreground\". Defaults to maximum
value of PixelType.

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::SetProjectionDimension "
";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::SetThresholdValue "

Set/Get the input value consider as \"threshold\". Defaults to NumericTraits<InputPixelType>::max()

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinaryThresholdProjectionImageFilter::~BinaryThresholdProjectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::BinomialBlurImageFilter "

Performs a separable blur on each dimension of an image.


The binomial blur consists of a nearest neighbor average along each
image dimension. The net result after n-iterations approaches
convolution with a gaussian.
See:
 itk::simple::BinomialBlur for the procedural interface

 itk::BinomialBlurImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBinomialBlurImageFilter.h
";

%feature("docstring")  itk::simple::BinomialBlurImageFilter::BinomialBlurImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BinomialBlurImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BinomialBlurImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BinomialBlurImageFilter::GetRepetitions "

Get and set the number of times to repeat the filter.

";

%feature("docstring")  itk::simple::BinomialBlurImageFilter::SetRepetitions "

Get and set the number of times to repeat the filter.

";

%feature("docstring")  itk::simple::BinomialBlurImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BinomialBlurImageFilter::~BinomialBlurImageFilter "

Destructor

";


%feature("docstring") itk::simple::BitwiseNotImageFilter "

Implements pixel-wise generic operation on one image.


This class is parameterized over the type of the input image and the
type of the output image. It is also parameterized by the operation to
be applied, using a Functor style.

UnaryFunctorImageFilter allows the output dimension of the filter to be larger than the input
dimension. Thus subclasses of the UnaryFunctorImageFilter (like the CastImageFilter ) can be used to promote a 2D image to a 3D image, etc.


See:
 UnaryGeneratorImageFilter

 BinaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::BitwiseNot for the procedural interface

 itk::UnaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBitwiseNotImageFilter.h
";

%feature("docstring")  itk::simple::BitwiseNotImageFilter::BitwiseNotImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BitwiseNotImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BitwiseNotImageFilter::Execute "
";

%feature("docstring")  itk::simple::BitwiseNotImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BitwiseNotImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BitwiseNotImageFilter::~BitwiseNotImageFilter "

Destructor

";


%feature("docstring") itk::simple::BlackTopHatImageFilter "

Black top hat extracts local minima that are smaller than the
structuring element.


Black top hat extracts local minima that are smaller than the
structuring element. It subtracts the background from the input image.
The output of the filter transforms the black valleys into white
peaks.

Top-hats are described in Chapter 4.5 of Pierre Soille's book
\"Morphological Image Analysis: Principles and Applications\", Second
Edition, Springer, 2003.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 itk::simple::BlackTopHat for the procedural interface

 itk::BlackTopHatImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBlackTopHatImageFilter.h
";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::BlackTopHatImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::GetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::SafeBorderOff "
";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::SafeBorderOn "

Set the value of SafeBorder to true or false respectfully.

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::SetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BlackTopHatImageFilter::~BlackTopHatImageFilter "

Destructor

";


%feature("docstring") itk::simple::BoundedReciprocalImageFilter "

Computes 1/(1+x) for each pixel in the image.


The filter expect both the input and output images to have the same
number of dimensions, and both of a scalar image type.
See:
 itk::simple::BoundedReciprocal for the procedural interface

 itk::BoundedReciprocalImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBoundedReciprocalImageFilter.h
";

%feature("docstring")  itk::simple::BoundedReciprocalImageFilter::BoundedReciprocalImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BoundedReciprocalImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BoundedReciprocalImageFilter::Execute "
";

%feature("docstring")  itk::simple::BoundedReciprocalImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BoundedReciprocalImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BoundedReciprocalImageFilter::~BoundedReciprocalImageFilter "

Destructor

";


%feature("docstring") itk::simple::BoxMeanImageFilter "

Implements a fast rectangular mean filter using the accumulator
approach.


This code was contributed in the Insight Journal paper: \"Efficient
implementation of kernel filtering\" by Beare R., Lehmann G https://hdl.handle.net/1926/555 http://www.insight-journal.org/browse/publication/160


Richard Beare

See:
 itk::simple::BoxMean for the procedural interface

 itk::BoxMeanImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBoxMeanImageFilter.h
";

%feature("docstring")  itk::simple::BoxMeanImageFilter::BoxMeanImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BoxMeanImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BoxMeanImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BoxMeanImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::BoxMeanImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::BoxMeanImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::BoxMeanImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BoxMeanImageFilter::~BoxMeanImageFilter "

Destructor

";


%feature("docstring") itk::simple::BoxSigmaImageFilter "

Implements a fast rectangular sigma filter using the accumulator
approach.


This code was contributed in the Insight Journal paper: \"Efficient
implementation of kernel filtering\" by Beare R., Lehmann G https://hdl.handle.net/1926/555 http://www.insight-journal.org/browse/publication/160


Gaetan Lehmann

See:
 itk::simple::BoxSigma for the procedural interface

 itk::BoxSigmaImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkBoxSigmaImageFilter.h
";

%feature("docstring")  itk::simple::BoxSigmaImageFilter::BoxSigmaImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::BoxSigmaImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::BoxSigmaImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::BoxSigmaImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::BoxSigmaImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::BoxSigmaImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::BoxSigmaImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::BoxSigmaImageFilter::~BoxSigmaImageFilter "

Destructor

";


%feature("docstring") itk::simple::CannyEdgeDetectionImageFilter "

This filter is an implementation of a Canny edge detector for scalar-
valued images.


Based on John Canny's paper \"A Computational Approach  to Edge
Detection\"(IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-8, No.6, November 1986), there are four major
steps used in the edge-detection scheme: (1) Smooth the input image
with Gaussian filter. (2) Calculate the second directional derivatives
of the smoothed image. (3) Non-Maximum Suppression: the zero-crossings
of 2nd derivative are found, and the sign of third derivative is used
to find the correct extrema. (4) The hysteresis thresholding is
applied to the gradient magnitude (multiplied with zero-crossings) of
the smoothed image to find and link edges.

Inputs and Outputs
The input to this filter should be a scalar, real-valued Itk image of
arbitrary dimension. The output should also be a scalar, real-value
Itk image of the same dimensionality.
Parameters
There are four parameters for this filter that control the sub-filters
used by the algorithm.

Variance and Maximum error are used in the Gaussian smoothing of the
input image. See itkDiscreteGaussianImageFilter for information on
these parameters.

Threshold is the lowest allowed value in the output image. Its data
type is the same as the data type of the output image. Any values
below the Threshold level will be replaced with the OutsideValue
parameter value, whose default is zero.
 TodoEdge-linking will be added when an itk connected component
labeling algorithm is available.


See:
 DiscreteGaussianImageFilter

 ZeroCrossingImageFilter

 ThresholdImageFilter

 itk::simple::CannyEdgeDetection for the procedural interface

 itk::CannyEdgeDetectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkCannyEdgeDetectionImageFilter.h
";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::CannyEdgeDetectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::GetLowerThreshold "
";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::GetMaximumError "

Set/Get the maximum error of the Gaussian smoothing kernel in each
dimensional direction.

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::GetUpperThreshold "
";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::GetVariance "

Set/Get the variance of the Gaussian smoothing filter.

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::SetLowerThreshold "
";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::SetMaximumError "

Set/Get the MaximumError parameter used by the Gaussian smoothing
filter in this algorithm

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::SetMaximumError "

Set the values of the MaximumError vector all to value

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::SetUpperThreshold "

Set the Threshold value for detected edges. TODO: Document in the
ITKv4 migration guide that the SetThreshold member function was
removed from the CannyEdgeDetectionImageFilter , and that both UpperThreshold and LowerThreshold need to be set. To
get the same results as with the SetThreshold method change
\"myfilter->SetThrehsold\" to \"myfilter->SetUpperThreshold\", and add
\"myfilter->SetLowerThreshold(GetUpperThreshold()/2.0)\".

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::SetVariance "

Set/Get the variance of the Gaussian smoothing filter.

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::SetVariance "

Set the values of the Variance vector all to value

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CannyEdgeDetectionImageFilter::~CannyEdgeDetectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::CannySegmentationLevelSetImageFilter "

Segments structures in images based on image features derived from
pseudo-canny-edges.


IMPORTANT
The SegmentationLevelSetImageFilter class and the CannySegmentationLevelSetFunction class contain additional information necessary to the full
understanding of how to use this filter.
OVERVIEW
This class is a level set method segmentation filter. It constructs a
speed function which is designed to lock onto edges as detected by a
Canny filter.

The CannySegmentationLevelSetImageFilter can be a tool for refining an existing segmentation, or it can be
used to try to segment a region by itself. Like all other level-set
based segmentation filters (see SegmentationLevelSetImageFilter ), it works by first constructing a scalar speed term and a vector
advection field based on edge features in the image. The level set
front is then moved according to these two terms with the addition of
a third curvature term to contol the smoothness of the solution.

The speed term is constructed as the Danielsson distance transform of
the Canny edge image, as calculated by the CannyEdgeDetectionImageFilter . This scalar speed can be tuned in and out of the final evolution
equation by setting the PropagationScaling parameter (a value of 0
removes the speed term).

The advection field term is constructed by minimizing Danielsson
distance squared. i.e. $ \\\\mbox{min} \\\\int D^2 \\\\Rightarrow D \\\\nabla D $ . This term moves the level set down the gradient of the distance
transform.

In practice, you may set the speed (propagation) term to zero if your
initialization is already close to the edge you are interested in. If
you are trying to segment a region by seeding with a small surface
(blob, sphere) then you will likely want to add speed (propagation) to
the equation so that the levelsets can expand along zero gradients.
The relative influence of these two terms are controlled by the
SetPropagationScaling and SetAdvectionScaling parameters.
INPUTS
This filter requires two inputs. The first input is a seed image. This
seed image must contain an isosurface that you want to use as the seed
for your segmentation. It can be a binary, graylevel, or floating
point image. The only requirement is that it contain a closed
isosurface that you will identify as the seed by setting the
IsosurfaceValue parameter of the filter. For a binary image you will
want to set your isosurface value halfway between your on and off
values (i.e. for 0's and 1's, use an isosurface value of 0.5).

The second input is the feature image. This is the image from which
the speed function will be calculated. For most applications, this is
the image that you want to segment. The desired isosurface in your
seed image should lie within the region of your feature image that you
are trying to segment.

See SegmentationLevelSetImageFilter for more information on Inputs.
OUTPUTS
The filter outputs a single, scalar, real-valued image. Positive
*values in the output image are inside the segmented region and
negative *values in the image are outside of the inside region. The
zero crossings of *the image correspond to the position of the level
set front.

See SparseFieldLevelSetImageFilter and SegmentationLevelSetImageFilter for more information.
PARAMETERS
There are five parameters important for controlling the behavior of
this filter.

(1) Threshold. Sets the thresholding value of the Canny edge
detection. See CannyEdgeDetectionImageFilter for more information.

(2) Variance. Controls the smoothing parameter of the gaussian
filtering done during Canny edge detection.

(3) CurvatureScaling. Controls the degree to which curvature
influences the evolution of the level set. Higher values relative to
Propagation and Advection scalings will yield a smoother surface.

(4) PropagationScaling. Scales the propagation (speed) term of the
level set equation. Set this term to zero to allow the level set to
flow only down the gradient of the distance transform.

(5) AdvectionScaling. Scales influence of the advection field relative
to curvature and propagation terms.

See:
 SegmentationLevelSetImageFilter

 CannySegmentationLevelSetFunction ,

 SparseFieldLevelSetImageFilter

 itk::simple::CannySegmentationLevelSet for the procedural interface

 itk::CannySegmentationLevelSetImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkCannySegmentationLevelSetImageFilter.h
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::CannySegmentationLevelSetImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::Execute "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetAdvectionScaling "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetCannyImage "

Get the Canny image that was used to create the speed and advection
images

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetCurvatureScaling "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetElapsedIterations "

Number of iterations run.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetIsoSurfaceValue "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetPropagationScaling "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetRMSChange "

The Root Mean Square of the levelset upon termination.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetThreshold "

Set the Threshold parameter of the CannyEdgeDetectionImageFilter used by the underlying level set function.

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::GetVariance "

Set the Variance parameter of the CannyEdgeDetectionImageFilter used by the underlying level set function.

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::ReverseExpansionDirectionOff "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::ReverseExpansionDirectionOn "

Set the value of ReverseExpansionDirection to true or false
respectfully.

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::SetAdvectionScaling "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::SetCurvatureScaling "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::SetIsoSurfaceValue "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::SetPropagationScaling "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::SetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::SetThreshold "

Set the Threshold parameter of the CannyEdgeDetectionImageFilter used by the underlying level set function.

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::SetVariance "

Set the Variance parameter of the CannyEdgeDetectionImageFilter used by the underlying level set function.

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CannySegmentationLevelSetImageFilter::~CannySegmentationLevelSetImageFilter "

Destructor

";


%feature("docstring") itk::simple::CastImageFilter "

A hybrid cast image filter to convert images to other types of images.


Several different ITK classes are implemented under the hood, to
convert between different image types.


See:
 itk::simple::Cast for the procedural interface


C++ includes: sitkCastImageFilter.h
";

%feature("docstring")  itk::simple::CastImageFilter::CastImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CastImageFilter::Execute "
";

%feature("docstring")  itk::simple::CastImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CastImageFilter::GetOutputPixelType "
";

%feature("docstring")  itk::simple::CastImageFilter::SetOutputPixelType "

Set/Get the output pixel type

";

%feature("docstring")  itk::simple::CastImageFilter::ToString "
";

%feature("docstring")  itk::simple::CastImageFilter::~CastImageFilter "
";


%feature("docstring") itk::simple::CenteredTransformInitializerFilter "

CenteredTransformInitializerFilter is a helper class intended to initialize the center of rotation and
the translation of Transforms having the center of rotation among
their parameters.


This class is connected to the fixed image, moving image and transform
involved in the registration. Two modes of operation are possible:


Geometrical,

Center of mass
 In the first mode, the geometrical center of the moving image is
passed as initial center of rotation to the transform and the vector
from the center of the fixed image to the center of the moving image
is passed as the initial translation. This mode basically assumes that
the anatomical objects to be registered are centered in their
respective images. Hence the best initial guess for the registration
is the one that superimposes those two centers.

In the second mode, the moments of gray level values are computed for
both images. The center of mass of the moving image is then used as
center of rotation. The vector between the two centers of mass is
passes as the initial translation to the transform. This second
approach assumes that the moments of the anatomical objects are
similar for both images and hence the best initial guess for
registration is to superimpose both mass centers. Note that this
assumption will probably not hold in multi-modality registration.


See:
 itk::CenteredTransformInitializer


C++ includes: sitkCenteredTransformInitializerFilter.h
";

%feature("docstring")  itk::simple::CenteredTransformInitializerFilter::CenteredTransformInitializerFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CenteredTransformInitializerFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CenteredTransformInitializerFilter::GeometryOn "

Select between using the geometrical center of the images or using the
center of mass given by the image intensities.

";

%feature("docstring")  itk::simple::CenteredTransformInitializerFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CenteredTransformInitializerFilter::GetOperationMode "
";

%feature("docstring")  itk::simple::CenteredTransformInitializerFilter::MomentsOn "

Select between using the geometrical center of the images or using the
center of mass given by the image intensities.

";

%feature("docstring")  itk::simple::CenteredTransformInitializerFilter::SetOperationMode "
";

%feature("docstring")  itk::simple::CenteredTransformInitializerFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CenteredTransformInitializerFilter::~CenteredTransformInitializerFilter "

Destructor

";


%feature("docstring") itk::simple::CenteredVersorTransformInitializerFilter "

CenteredVersorTransformInitializerFilter is a helper class intended to initialize the center of rotation,
versor, and translation of the VersorRigid3DTransform.


This class derived from the CenteredTransformInitializerand uses it in
a more constrained context. It always uses the Moments mode, and also
takes advantage of the second order moments in order to initialize the
Versorrepresenting rotation.


See:
 itk::CenteredVersorTransformInitializer for the Doxygen on the original ITK class.


C++ includes: sitkCenteredVersorTransformInitializerFilter.h
";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializerFilter::CenteredVersorTransformInitializerFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializerFilter::ComputeRotationOff "
";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializerFilter::ComputeRotationOn "

Set the value of ComputeRotation to true or false respectfully.

";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializerFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializerFilter::GetComputeRotation "

Enable the use of the principal axes of each image to compute an
initial rotation that will align them.

";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializerFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializerFilter::SetComputeRotation "

Enable the use of the principal axes of each image to compute an
initial rotation that will align them.

";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializerFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializerFilter::~CenteredVersorTransformInitializerFilter "

Destructor

";


%feature("docstring") itk::simple::ChangeLabelImageFilter "

Change Sets of Labels.


This filter produces an output image whose pixels are either copied
from the input if they are not being changed or are rewritten based on
the change parameters

This filter is templated over the input image type and the output
image type.

The filter expect both images to have the same number of dimensions.


Tim Kelliher. GE Research, Niskayuna, NY.

This work was supported by a grant from DARPA, executed by the U.S.
Army Medical Research and Materiel Command/TATRC Assistance Agreement,
Contract::W81XWH-05-2-0059.

See:
 itk::simple::ChangeLabel for the procedural interface

 itk::ChangeLabelImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkChangeLabelImageFilter.h
";

%feature("docstring")  itk::simple::ChangeLabelImageFilter::ChangeLabelImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ChangeLabelImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ChangeLabelImageFilter::Execute "
";

%feature("docstring")  itk::simple::ChangeLabelImageFilter::GetChangeMap "
";

%feature("docstring")  itk::simple::ChangeLabelImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ChangeLabelImageFilter::SetChangeMap "

Set the entire change map

";

%feature("docstring")  itk::simple::ChangeLabelImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ChangeLabelImageFilter::~ChangeLabelImageFilter "

Destructor

";


%feature("docstring") itk::simple::ChangeLabelLabelMapFilter "

Replace the label Ids of selected LabelObjects with new label Ids.


This filter takes as input a label map and a list of pairs of Label Ids, to produce as output a new label map where the label Ids have
been replaced according to the pairs in the list.

Labels that are relabeled to the same label Id are automatically
merged and optimized into a single LabelObject . The background label can also be changed. Any object relabeled to
the output background will automatically be removed.

This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ShapeLabelObject , RelabelComponentImageFilter , ChangeLabelImageFilter

 itk::simple::ChangeLabelLabelMapFilter for the procedural interface

 itk::ChangeLabelLabelMapFilter for the Doxygen on the original ITK class.


C++ includes: sitkChangeLabelLabelMapFilter.h
";

%feature("docstring")  itk::simple::ChangeLabelLabelMapFilter::ChangeLabelLabelMapFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ChangeLabelLabelMapFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ChangeLabelLabelMapFilter::Execute "
";

%feature("docstring")  itk::simple::ChangeLabelLabelMapFilter::GetChangeMap "
";

%feature("docstring")  itk::simple::ChangeLabelLabelMapFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ChangeLabelLabelMapFilter::SetChangeMap "
";

%feature("docstring")  itk::simple::ChangeLabelLabelMapFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ChangeLabelLabelMapFilter::~ChangeLabelLabelMapFilter "

Destructor

";


%feature("docstring") itk::simple::CheckerBoardImageFilter "

Combines two images in a checkerboard pattern.


CheckerBoardImageFilter takes two input images that must have the same dimension, size,
origin and spacing and produces an output image of the same size by
combining the pixels from the two input images in a checkerboard
pattern. This filter is commonly used for visually comparing two
images, in particular for evaluating the results of an image
registration process.

This filter is implemented as a multithreaded filter. It provides a
DynamicThreadedGenerateData() method for its implementation.
See:
 itk::simple::CheckerBoard for the procedural interface

 itk::CheckerBoardImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkCheckerBoardImageFilter.h
";

%feature("docstring")  itk::simple::CheckerBoardImageFilter::CheckerBoardImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CheckerBoardImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::CheckerBoardImageFilter::GetCheckerPattern "

Set/Get the checker pattern array, i.e. the number of checker boxes
per image dimension.

";

%feature("docstring")  itk::simple::CheckerBoardImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CheckerBoardImageFilter::SetCheckerPattern "

Set/Get the checker pattern array, i.e. the number of checker boxes
per image dimension.

";

%feature("docstring")  itk::simple::CheckerBoardImageFilter::SetCheckerPattern "

Set the values of the CheckerPattern vector all to value

";

%feature("docstring")  itk::simple::CheckerBoardImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CheckerBoardImageFilter::~CheckerBoardImageFilter "

Destructor

";


%feature("docstring") itk::simple::ClampImageFilter "

Casts input pixels to output pixel type and clamps the output pixel
values to a specified range.


Default range corresponds to the range supported by the pixel type of
the output image.

This filter is templated over the input image type and the output
image type.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 UnaryFunctorImageFilter

 CastImageFilter

 itk::simple::Clamp for the procedural interface

 itk::ClampImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkClampImageFilter.h
";

%feature("docstring")  itk::simple::ClampImageFilter::ClampImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ClampImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ClampImageFilter::Execute "
";

%feature("docstring")  itk::simple::ClampImageFilter::GetLowerBound "
";

%feature("docstring")  itk::simple::ClampImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ClampImageFilter::GetOutputPixelType "
";

%feature("docstring")  itk::simple::ClampImageFilter::GetUpperBound "
";

%feature("docstring")  itk::simple::ClampImageFilter::SetLowerBound "
";

%feature("docstring")  itk::simple::ClampImageFilter::SetOutputPixelType "
";

%feature("docstring")  itk::simple::ClampImageFilter::SetUpperBound "
";

%feature("docstring")  itk::simple::ClampImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ClampImageFilter::~ClampImageFilter "

Destructor

";


%feature("docstring") itk::simple::ClosingByReconstructionImageFilter "

Closing by reconstruction of an image.


This filter is similar to the morphological closing, but contrary to
the morphological closing, the closing by reconstruction preserves the
shape of the components. The closing by reconstruction of an image
\"f\" is defined as:

ClosingByReconstruction(f) = ErosionByReconstruction(f, Dilation(f)).

Closing by reconstruction not only preserves structures preserved by
the dilation, but also levels raises the contrast of the darkest
regions. If PreserveIntensities is on, a subsequent reconstruction by
dilation using a marker image that is the original image for all
unaffected pixels.

Closing by reconstruction is described in Chapter 6.3.9 of Pierre
Soille's book \"Morphological Image Analysis: Principles and
Applications\", Second Edition, Springer, 2003.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 GrayscaleMorphologicalClosingImageFilter

 itk::simple::ClosingByReconstruction for the procedural interface

 itk::ClosingByReconstructionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkClosingByReconstructionImageFilter.h
";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::ClosingByReconstructionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::GetPreserveIntensities "

Set/Get whether the original intensities of the image retained for
those pixels unaffected by the opening by reconstrcution. If Off, the
output pixel contrast will be reduced.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::PreserveIntensitiesOff "
";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::PreserveIntensitiesOn "

Set the value of PreserveIntensities to true or false respectfully.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::SetPreserveIntensities "

Set/Get whether the original intensities of the image retained for
those pixels unaffected by the opening by reconstrcution. If Off, the
output pixel contrast will be reduced.

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ClosingByReconstructionImageFilter::~ClosingByReconstructionImageFilter "

Destructor

";


%feature("docstring") itk::simple::CollidingFrontsImageFilter "

Selects a region of space where two independent fronts run towards
each other.


The filter can be used to quickly segment anatomical structures (e.g.
for level set initialization).

The filter uses two instances of FastMarchingUpwindGradientImageFilter to compute the gradients of arrival times of two wavefronts
propagating from two sets of seeds. The input of the filter is used as
the speed of the two wavefronts. The output is the dot product between
the two gradient vector fields.

The filter works on the following basic idea. In the regions where the
dot product between the two gradient fields is negative, the two
fronts propagate in opposite directions. In the regions where the dot
product is positive, the two fronts propagate in the same direction.
This can be used to extract the region of space between two sets of
points.

If StopOnTargets is On, then each front will stop as soon as all seeds
of the other front have been reached. This can markedly speed up the
execution of the filter, since wave propagation does not take place on
the complete image.

Optionally, a connectivity criterion can be applied to the resulting
dot product image. In this case, the only negative region in the
output image is the one connected to the seeds.


Luca Antiga Ph.D. Biomedical Technologies Laboratory, Bioengineering
Department, Mario Negri Institute, Italy.

See:
 itk::simple::CollidingFronts for the procedural interface

 itk::CollidingFrontsImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkCollidingFrontsImageFilter.h
";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::AddSeedPoint1 "

Add SeedPoints1 point.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::AddSeedPoint2 "

Add SeedPoints2 point.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::ApplyConnectivityOff "
";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::ApplyConnectivityOn "

Set the value of ApplyConnectivity to true or false respectfully.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::ClearSeedPoints1 "

Remove all SeedPoints1 points.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::ClearSeedPoints2 "

Remove all SeedPoints2 points.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::CollidingFrontsImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::GetApplyConnectivity "
";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::GetNegativeEpsilon "
";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::GetSeedPoints1 "

Get the container of Seed Points representing the first initial front.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::GetSeedPoints2 "

Get the container of Seed Points representing the second initial
front.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::GetStopOnTargets "
";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::SetApplyConnectivity "
";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::SetNegativeEpsilon "
";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::SetSeedPoints1 "

Set the container of Seed Points representing the first initial front.
Seed points are represented as a VectorContainer of LevelSetNodes.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::SetSeedPoints2 "

Set the container of Seed Points representing the second initial
front. Seed points are represented as a VectorContainer of LevelSetNodes.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::SetStopOnTargets "
";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::StopOnTargetsOff "
";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::StopOnTargetsOn "

Set the value of StopOnTargets to true or false respectfully.

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CollidingFrontsImageFilter::~CollidingFrontsImageFilter "

Destructor

";


%feature("docstring") itk::simple::Command "

An implementation of the Command design pattern for callback.


This class provides a callback mechanism for event that occur from the ProcessObject. These commands can be utilized to observe these events.

The Command can be created on the stack, and will automatically unregistered it's
self when destroyed.

For more information see the page CommandPage.

C++ includes: sitkCommand.h
";

%feature("docstring")  itk::simple::Command::Command "

Default Constructor.

";

%feature("docstring")  itk::simple::Command::Execute "

The method that defines action to be taken by the command

";

%feature("docstring")  itk::simple::Command::GetName "

Set/Get Command Name

";

%feature("docstring")  itk::simple::Command::SetName "
";

%feature("docstring")  itk::simple::Command::~Command "

Destructor.

";


%feature("docstring") itk::simple::ComplexToImaginaryImageFilter "

Computes pixel-wise the imaginary part of a complex image.



See:
 itk::simple::ComplexToImaginary for the procedural interface

 itk::ComplexToImaginaryImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkComplexToImaginaryImageFilter.h
";

%feature("docstring")  itk::simple::ComplexToImaginaryImageFilter::ComplexToImaginaryImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ComplexToImaginaryImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ComplexToImaginaryImageFilter::Execute "
";

%feature("docstring")  itk::simple::ComplexToImaginaryImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ComplexToImaginaryImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ComplexToImaginaryImageFilter::~ComplexToImaginaryImageFilter "

Destructor

";


%feature("docstring") itk::simple::ComplexToModulusImageFilter "

Computes pixel-wise the Modulus of a complex image.



See:
 itk::simple::ComplexToModulus for the procedural interface

 itk::ComplexToModulusImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkComplexToModulusImageFilter.h
";

%feature("docstring")  itk::simple::ComplexToModulusImageFilter::ComplexToModulusImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ComplexToModulusImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ComplexToModulusImageFilter::Execute "
";

%feature("docstring")  itk::simple::ComplexToModulusImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ComplexToModulusImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ComplexToModulusImageFilter::~ComplexToModulusImageFilter "

Destructor

";


%feature("docstring") itk::simple::ComplexToPhaseImageFilter "

Computes pixel-wise the modulus of a complex image.



See:
 itk::simple::ComplexToPhase for the procedural interface

 itk::ComplexToPhaseImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkComplexToPhaseImageFilter.h
";

%feature("docstring")  itk::simple::ComplexToPhaseImageFilter::ComplexToPhaseImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ComplexToPhaseImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ComplexToPhaseImageFilter::Execute "
";

%feature("docstring")  itk::simple::ComplexToPhaseImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ComplexToPhaseImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ComplexToPhaseImageFilter::~ComplexToPhaseImageFilter "

Destructor

";


%feature("docstring") itk::simple::ComplexToRealImageFilter "

Computes pixel-wise the real(x) part of a complex image.



See:
 itk::simple::ComplexToReal for the procedural interface

 itk::ComplexToRealImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkComplexToRealImageFilter.h
";

%feature("docstring")  itk::simple::ComplexToRealImageFilter::ComplexToRealImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ComplexToRealImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ComplexToRealImageFilter::Execute "
";

%feature("docstring")  itk::simple::ComplexToRealImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ComplexToRealImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ComplexToRealImageFilter::~ComplexToRealImageFilter "

Destructor

";


%feature("docstring") itk::simple::ComposeImageFilter "

ComposeImageFilter combine several scalar images into a multicomponent image.


ComposeImageFilter combine several scalar images into an itk::Image of vector pixel ( itk::Vector , itk::RGBPixel , ...), of std::complex pixel, or in an itk::VectorImage .

Inputs and Usage
 All input images are expected to have the same template parameters
and have the same size and origin.

See:
 VectorImage

 VectorIndexSelectionCastImageFilter

 itk::simple::Compose for the procedural interface


C++ includes: sitkComposeImageFilter.h
";

%feature("docstring")  itk::simple::ComposeImageFilter::ComposeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ComposeImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::ComposeImageFilter::Execute "
";

%feature("docstring")  itk::simple::ComposeImageFilter::Execute "
";

%feature("docstring")  itk::simple::ComposeImageFilter::Execute "
";

%feature("docstring")  itk::simple::ComposeImageFilter::Execute "
";

%feature("docstring")  itk::simple::ComposeImageFilter::Execute "
";

%feature("docstring")  itk::simple::ComposeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ComposeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ComposeImageFilter::~ComposeImageFilter "

Destructor

";


%feature("docstring") itk::simple::CompositeTransform "

This class contains a stack of transforms and concatenates them by
composition.


The transforms are composed in reverse order with the back being applied first: $ T_0 o T_1 = T_0(T_1(x)) $ Transforms are stored in a queue, in the following order: $ T_0, T_1, ... , T_N-1 $

Transforms are added via AddTransform(). This adds the transforms to the back of the queue.

The only parameters of the transform at the back of the queue are
exposed and optimizable for registration.

Inverse: The inverse transform is created by retrieving the inverse
from each sub transform and adding them to a composite transform in
reverse order. The m_TransformsToOptimizeFlags is copied in reverse
for the inverse.


See:
 itk::CompositeTransform


C++ includes: sitkCompositeTransform.h
";

%feature("docstring")  itk::simple::CompositeTransform::AddTransform "

Add a transform to the back of the stack.


A deep-copy of the transform is performed. The added transform will
have the optimizable parameters, while the other parameters are part
of the fixed parameters.

";

%feature("docstring")  itk::simple::CompositeTransform::ClearTransforms "

Remove all transforms from the stack.

";

%feature("docstring")  itk::simple::CompositeTransform::CompositeTransform "

Construct an empty CompositeTransform.


The created CompositeTransform is initialized with zero transforms. Additional transforms of
dimensions can be added.

";

%feature("docstring")  itk::simple::CompositeTransform::CompositeTransform "

Create CompositeTransform converted or holding the transform argument.


If the Transform is internally a CompositeTransform, a shallow copy to the internal transform will be made. Otherwise a
new CompositeTransform is constructed which holds the transform argument.

";

%feature("docstring")  itk::simple::CompositeTransform::CompositeTransform "

A lazy copy constructor.


The new SimpleITK object will reference to the same underlying ITK CompositeTransform. A deep-copy will be made when the object is modified.

";

%feature("docstring")  itk::simple::CompositeTransform::CompositeTransform "

Create a composite from a vector of Transform.


The CompositeTransform is constructed from deep copies of the Transforms. If the vector
contains additional composite transforms, deep copies will be made and
nested composite transforms will be constructed.

An exception is thrown if the vector is empty.

";

%feature("docstring")  itk::simple::CompositeTransform::FlattenTransform "

Removes nested composite transforms.


If this transform contains additional composite transforms, then these
nested composite transformed are removed, while preserving the order
of the regular transforms and transferring ownership to the parent CompositeTransform.

Nested composite transform may not be written to a file.

";

%feature("docstring")  itk::simple::CompositeTransform::GetBackTransform "

Get a copy of the back transform.


If the stack is empty an exception will be thrown.

";

%feature("docstring")  itk::simple::CompositeTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CompositeTransform::GetNthTransform "

Get a copy of a transform in the stack.


If n is equal or greater than the number of transforms, then an
exception will be thrown.

";

%feature("docstring")  itk::simple::CompositeTransform::GetNumberOfTransforms "

The number of transforms in the stack.

";

%feature("docstring")  itk::simple::CompositeTransform::RemoveTransform "

Remove the active transform at the back.


If the stack is empty an exception will be thrown.

";

%feature("docstring")  itk::simple::CompositeTransform::~CompositeTransform "
";


%feature("docstring") itk::simple::ConfidenceConnectedImageFilter "

Segment pixels with similar statistics using connectivity.


This filter extracts a connected set of pixels whose pixel intensities
are consistent with the pixel statistics of a seed point. The mean and
variance across a neighborhood (8-connected, 26-connected, etc.) are
calculated for a seed point. Then pixels connected to this seed point
whose values are within the confidence interval for the seed point are
grouped. The width of the confidence interval is controlled by the
\"Multiplier\" variable (the confidence interval is the mean plus or
minus the \"Multiplier\" times the standard deviation). If the
intensity variations across a segment were gaussian, a \"Multiplier\"
setting of 2.5 would define a confidence interval wide enough to
capture 99% of samples in the segment.

After this initial segmentation is calculated, the mean and variance
are re-calculated. All the pixels in the previous segmentation are
used to calculate the mean the standard deviation (as opposed to using
the pixels in the neighborhood of the seed point). The segmentation is
then recalculated using these refined estimates for the mean and
variance of the pixel values. This process is repeated for the
specified number of iterations. Setting the \"NumberOfIterations\" to
zero stops the algorithm after the initial segmentation from the seed
point.

NOTE: the lower and upper threshold are restricted to lie within the
valid numeric limits of the input data pixel type. Also, the limits
may be adjusted to contain the seed point's intensity.
See:
 itk::simple::ConfidenceConnected for the procedural interface

 itk::ConfidenceConnectedImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkConfidenceConnectedImageFilter.h
";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::AddSeed "

Add SeedList point.

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::ClearSeeds "

Remove all SeedList points.

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::ConfidenceConnectedImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::GetInitialNeighborhoodRadius "

Get/Set the radius of the neighborhood over which the statistics are
evaluated

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::GetMean "

Method to get access to the mean of the pixels accepted in the output
region. This method should only be invoked after the filter has been
executed using the Update() method.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::GetMultiplier "

Set/Get the multiplier to define the confidence interval. Multiplier
can be anything greater than zero. A typical value is 2.5

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::GetNumberOfIterations "

Set/Get the number of iterations

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::GetReplaceValue "

Set/Get value to replace thresholded pixels

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::GetSeedList "

Get list of seeds.

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::GetVariance "

Method to get access to the variance of the pixels accepted in the
output region. This method should only be invoked after the filter has
been executed using the Update() method.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::SetInitialNeighborhoodRadius "

Get/Set the radius of the neighborhood over which the statistics are
evaluated

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::SetMultiplier "

Set/Get the multiplier to define the confidence interval. Multiplier
can be anything greater than zero. A typical value is 2.5

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::SetNumberOfIterations "

Set/Get the number of iterations

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::SetReplaceValue "

Set/Get value to replace thresholded pixels

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::SetSeedList "

Set list of image indexes for seeds.

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ConfidenceConnectedImageFilter::~ConfidenceConnectedImageFilter "

Destructor

";


%feature("docstring") itk::simple::ConnectedComponentImageFilter "

Label the objects in a binary image.


ConnectedComponentImageFilter labels the objects in a binary image (non-zero pixels are considered
to be objects, zero-valued pixels are considered to be background).
Each distinct object is assigned a unique label. The filter
experiments with some improvements to the existing implementation, and
is based on run length encoding along raster lines. If the output
background value is set to zero (the default), the final object labels
start with 1 and are consecutive. If the output background is set to a
non-zero value (by calling the SetBackgroundValue() routine of the
filter), the final labels start at 0, and remain consecutive except
for skipping the background value as needed. Objects that are reached
earlier by a raster order scan have a lower label. This is different
to the behaviour of the original connected component image filter
which did not produce consecutive labels or impose any particular
ordering.

After the filter is executed, ObjectCount holds the number of
connected components.


See:
 ImageToImageFilter

 itk::simple::ConnectedComponent for the procedural interface

 itk::ConnectedComponentImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkConnectedComponentImageFilter.h
";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::ConnectedComponentImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::Execute "
";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::GetObjectCount "

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ConnectedComponentImageFilter::~ConnectedComponentImageFilter "

Destructor

";


%feature("docstring") itk::simple::ConnectedThresholdImageFilter "

Label pixels that are connected to a seed and lie within a range of values.


ConnectedThresholdImageFilter labels pixels with ReplaceValue that are connected to an initial Seed
AND lie within a Lower and Upper threshold range.
See:
 itk::simple::ConnectedThreshold for the procedural interface

 itk::ConnectedThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkConnectedThresholdImageFilter.h
";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::AddSeed "

Add SeedList point.

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::ClearSeeds "

Remove all SeedList points.

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::ConnectedThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::GetConnectivity "

Type of connectivity to use (fully connected OR 4(2D), 6(3D), 2*N(ND)
connectivity).

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::GetLower "

Get Upper and Lower Threshold inputs as values.

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::GetReplaceValue "

Set/Get value to replace thresholded pixels. Pixels that lie * within
Lower and Upper (inclusive) will be replaced with this value. The
default is 1.

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::GetSeedList "

Get list of seeds.

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::GetUpper "

Get Upper and Lower Threshold inputs as values.

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::SetConnectivity "

Type of connectivity to use (fully connected OR 4(2D), 6(3D), 2*N(ND)
connectivity).

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::SetLower "

Set Upper and Lower Threshold inputs as values

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::SetReplaceValue "

Set/Get value to replace thresholded pixels. Pixels that lie * within
Lower and Upper (inclusive) will be replaced with this value. The
default is 1.

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::SetSeedList "

Set list of image indexes for seeds.

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::SetUpper "

Set Upper and Lower Threshold inputs as values

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ConnectedThresholdImageFilter::~ConnectedThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::ConstantPadImageFilter "

Increase the image size by padding with a constant value.


ConstantPadImageFilter changes the output image region. If the output image region is larger
than the input image region, the extra pixels are filled in by a
constant value. The output image region must be specified.

Visual explanation of padding regions.

This filter is implemented as a multithreaded filter. It provides a
DynamicThreadedGenerateData() method for its implementation.


See:
 WrapPadImageFilter , MirrorPadImageFilter

 itk::simple::ConstantPad for the procedural interface

 itk::ConstantPadImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkConstantPadImageFilter.h
";

%feature("docstring")  itk::simple::ConstantPadImageFilter::ConstantPadImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ConstantPadImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ConstantPadImageFilter::GetConstant "

Set/Get the pad value. Default is Zero.

";

%feature("docstring")  itk::simple::ConstantPadImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ConstantPadImageFilter::GetPadLowerBound "
";

%feature("docstring")  itk::simple::ConstantPadImageFilter::GetPadUpperBound "
";

%feature("docstring")  itk::simple::ConstantPadImageFilter::SetConstant "

Set/Get the pad value. Default is Zero.

";

%feature("docstring")  itk::simple::ConstantPadImageFilter::SetPadLowerBound "
";

%feature("docstring")  itk::simple::ConstantPadImageFilter::SetPadUpperBound "
";

%feature("docstring")  itk::simple::ConstantPadImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ConstantPadImageFilter::~ConstantPadImageFilter "

Destructor

";


%feature("docstring") itk::simple::ConvolutionImageFilter "

Convolve a given image with an arbitrary image kernel.


This filter operates by centering the flipped kernel at each pixel in
the image and computing the inner product between pixel values in the
image and pixel values in the kernel. The center of the kernel is
defined as $ \\\\lfloor (2*i+s-1)/2 \\\\rfloor $ where $i$ is the index and $s$ is the size of the largest possible region of the kernel image. For
kernels with odd sizes in all dimensions, this corresponds to the
center pixel. If a dimension of the kernel image has an even size,
then the center index of the kernel in that dimension will be the
largest integral index that is less than the continuous index of the
image center.

The kernel can optionally be normalized to sum to 1 using NormalizeOn() . Normalization is off by default.


WARNING:
This filter ignores the spacing, origin, and orientation of the kernel
image and treats them as identical to those in the input image.
 This code was contributed in the Insight Journal paper:

\"Image Kernel Convolution\" by Tustison N., Gee J. https://hdl.handle.net/1926/1323 http://www.insight-journal.org/browse/publication/208


Nicholas J. Tustison

James C. Gee

See:
 itk::simple::Convolution for the procedural interface

 itk::ConvolutionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkConvolutionImageFilter.h
";

%feature("docstring")  itk::simple::ConvolutionImageFilter::ConvolutionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ConvolutionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ConvolutionImageFilter::GetBoundaryCondition "
";

%feature("docstring")  itk::simple::ConvolutionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ConvolutionImageFilter::GetNormalize "
";

%feature("docstring")  itk::simple::ConvolutionImageFilter::GetOutputRegionMode "
";

%feature("docstring")  itk::simple::ConvolutionImageFilter::NormalizeOff "
";

%feature("docstring")  itk::simple::ConvolutionImageFilter::NormalizeOn "

Set the value of Normalize to true or false respectfully.

";

%feature("docstring")  itk::simple::ConvolutionImageFilter::SetBoundaryCondition "
";

%feature("docstring")  itk::simple::ConvolutionImageFilter::SetNormalize "

Normalize the output image by the sum of the kernel components

";

%feature("docstring")  itk::simple::ConvolutionImageFilter::SetOutputRegionMode "
";

%feature("docstring")  itk::simple::ConvolutionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ConvolutionImageFilter::~ConvolutionImageFilter "

Destructor

";


%feature("docstring") itk::simple::CosImageFilter "

Computes the cosine of each pixel.


This filter is templated over the pixel type of the input image and
the pixel type of the output image.

The filter walks over all of the pixels in the input image, and for
each pixel does the following:


cast the pixel value to double ,

apply the std::cos() function to the double value,

cast the double value resulting from std::cos() to the pixel type of
the output image,

store the cast value into the output image.
 The filter expects both images to have the same dimension (e.g. both
2D, or both 3D, or both ND)
See:
 itk::simple::Cos for the procedural interface

 itk::CosImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkCosImageFilter.h
";

%feature("docstring")  itk::simple::CosImageFilter::CosImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CosImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CosImageFilter::Execute "
";

%feature("docstring")  itk::simple::CosImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CosImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CosImageFilter::~CosImageFilter "

Destructor

";


%feature("docstring") itk::simple::CropImageFilter "

Decrease the image size by cropping the image by an itk::Size at both the upper and lower bounds of the largest possible region.


CropImageFilter changes the image boundary of an image by removing pixels outside the
target region. The target region is not specified in advance, but
calculated in BeforeThreadedGenerateData() .

This filter uses ExtractImageFilter to perform the cropping.
See:
 itk::simple::Crop for the procedural interface

 itk::CropImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkCropImageFilter.h
";

%feature("docstring")  itk::simple::CropImageFilter::CropImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CropImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CropImageFilter::Execute "
";

%feature("docstring")  itk::simple::CropImageFilter::GetLowerBoundaryCropSize "

Set/Get the cropping sizes for the upper and lower boundaries.

";

%feature("docstring")  itk::simple::CropImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CropImageFilter::GetUpperBoundaryCropSize "

Set/Get the cropping sizes for the upper and lower boundaries.

";

%feature("docstring")  itk::simple::CropImageFilter::SetLowerBoundaryCropSize "

Set/Get the cropping sizes for the upper and lower boundaries.

";

%feature("docstring")  itk::simple::CropImageFilter::SetUpperBoundaryCropSize "

Set/Get the cropping sizes for the upper and lower boundaries.

";

%feature("docstring")  itk::simple::CropImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CropImageFilter::~CropImageFilter "

Destructor

";


%feature("docstring") itk::simple::CurvatureAnisotropicDiffusionImageFilter "

This filter performs anisotropic diffusion on a scalar itk::Image using the modified curvature diffusion equation (MCDE) implemented in
itkCurvatureNDAnisotropicDiffusionFunction. For detailed information
on anisotropic diffusion and the MCDE see
itkAnisotropicDiffusionFunction and
itkCurvatureNDAnisotropicDiffusionFunction.

Inputs and Outputs
The input and output to this filter must be a scalar itk::Image with numerical pixel types (float or double). A user defined type
which correctly defines arithmetic operations with floating point
accuracy should also give correct results.
Parameters
Please first read all the documentation found in AnisotropicDiffusionImageFilter and AnisotropicDiffusionFunction . Also see CurvatureNDAnisotropicDiffusionFunction .
 The default time step for this filter is set to the maximum
theoretically stable value: 0.5 / 2^N, where N is the dimensionality
of the image. For a 2D image, this means valid time steps are below
0.1250. For a 3D image, valid time steps are below 0.0625.


See:
 AnisotropicDiffusionImageFilter

 AnisotropicDiffusionFunction

 CurvatureNDAnisotropicDiffusionFunction

 itk::simple::CurvatureAnisotropicDiffusion for the procedural interface

 itk::CurvatureAnisotropicDiffusionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkCurvatureAnisotropicDiffusionImageFilter.h
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::CurvatureAnisotropicDiffusionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::EstimateOptimalTimeStep "

This method autmatically sets the optimal timestep for an image given
its spacing.

";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::Execute "
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::GetConductanceParameter "
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::GetConductanceScalingUpdateInterval "
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::GetTimeStep "
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::SetConductanceParameter "
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::SetConductanceScalingUpdateInterval "
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::SetTimeStep "
";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CurvatureAnisotropicDiffusionImageFilter::~CurvatureAnisotropicDiffusionImageFilter "

Destructor

";


%feature("docstring") itk::simple::CurvatureFlowImageFilter "

Denoise an image using curvature driven flow.


CurvatureFlowImageFilter implements a curvature driven image denoising algorithm. Iso-
brightness contours in the grayscale input image are viewed as a level
set. The level set is then evolved using a curvature-based speed
function:

\\\\[ I_t = \\\\kappa |\\\\nabla I| \\\\] where $ \\\\kappa $ is the curvature.

The advantage of this approach is that sharp boundaries are preserved
with smoothing occurring only within a region. However, it should be
noted that continuous application of this scheme will result in the
eventual removal of all information as each contour shrinks to zero
and disappear.

Note that unlike level set segmentation algorithms, the image to be
denoised is already the level set and can be set directly as the input
using the SetInput() method.

This filter has two parameters: the number of update iterations to be
performed and the timestep between each update.

The timestep should be \"small enough\" to ensure numerical stability.
Stability is guarantee when the timestep meets the CFL (Courant-
Friedrichs-Levy) condition. Broadly speaking, this condition ensures
that each contour does not move more than one grid position at each
timestep. In the literature, the timestep is typically user specified
and have to manually tuned to the application.

This filter make use of the multi-threaded finite difference solver
hierarchy. Updates are computed using a CurvatureFlowFunction object. A zero flux Neumann boundary condition when computing
derivatives near the data boundary.

This filter may be streamed. To support streaming this filter produces
a padded output which takes into account edge effects. The size of the
padding is m_NumberOfIterations on each edge. Users of this filter
should only make use of the center valid central region.


WARNING:
This filter assumes that the input and output types have the same
dimensions. This filter also requires that the output image pixels are
of a floating point type. This filter works for any dimensional
images.
 Reference: \"Level Set Methods and Fast Marching Methods\", J.A.
Sethian, Cambridge Press, Chapter 16, Second edition, 1999.


See:
 DenseFiniteDifferenceImageFilter

 CurvatureFlowFunction

 MinMaxCurvatureFlowImageFilter

 BinaryMinMaxCurvatureFlowImageFilter

 itk::simple::CurvatureFlow for the procedural interface

 itk::CurvatureFlowImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkCurvatureFlowImageFilter.h
";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::CurvatureFlowImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::Execute "
";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::GetTimeStep "

Get the timestep parameter.

";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::SetTimeStep "

Set the timestep parameter.

";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CurvatureFlowImageFilter::~CurvatureFlowImageFilter "

Destructor

";


%feature("docstring") itk::simple::CyclicShiftImageFilter "

Perform a cyclic spatial shift of image intensities on the image grid.


This filter supports arbitrary cyclic shifts of pixel values on the
image grid. If the Shift is set to [xOff, yOff], the value of the
pixel at [0, 0] in the input image will be the value of the pixel in
the output image at index [xOff modulo xSize, yOff modulo ySize] where
xSize and ySize are the sizes of the image in the x and y dimensions,
respectively. If a pixel value is moved across a boundary, the pixel
value is wrapped around that boundary. For example, if the image is
40-by-40 and the Shift is [13, 47], then the value of the pixel at [0,
0] in the input image will be the value of the pixel in the output
image at index [13, 7].

Negative Shifts are supported. This filter also works with images
whose largest possible region starts at a non-zero index.
See:
 itk::simple::CyclicShift for the procedural interface

 itk::CyclicShiftImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkCyclicShiftImageFilter.h
";

%feature("docstring")  itk::simple::CyclicShiftImageFilter::CyclicShiftImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::CyclicShiftImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::CyclicShiftImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::CyclicShiftImageFilter::GetShift "

Set/get the shift. Shifts may be positive or negative.

";

%feature("docstring")  itk::simple::CyclicShiftImageFilter::SetShift "

Set/get the shift. Shifts may be positive or negative.

";

%feature("docstring")  itk::simple::CyclicShiftImageFilter::SetShift "

Set the values of the Shift vector all to value

";

%feature("docstring")  itk::simple::CyclicShiftImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::CyclicShiftImageFilter::~CyclicShiftImageFilter "

Destructor

";


%feature("docstring") itk::simple::DICOMOrientImageFilter "

Permute axes and flip images as needed to obtain an approximation to
the desired orientation.


The physical location of all pixels in the image remains the same, but
the meta-data and the ordering of the stored pixels may change.

DICOMOrientImageFilter depends on a set of constants that describe all possible labels.
Directions are labeled in terms of following pairs:


Left and Right (Subject's left and right)

Anterior and Posterior (Subject's front and back)

Inferior and Superior (Subject's bottom and top, i.e. feet and head)
 The initials of these directions are used in a 3 letter code in the
enumerated type OrientationEnum. The initials are given fastest moving
index first, second fastest second, third fastest third, where the
label's direction indicates increasing values.

An ITK image with an identity direction cosine matrix is in LPS (Left,
Posterior, Superior) orientation as defined by the DICOM standard.

\\\\[ LPS = \\\\begin{Bmatrix} from\\\\ right\\\\ to\\\\
\\\\textbf{L}eft \\\\\\\\ from\\\\ anterior\\\\ towards\\\\
\\\\textbf{P}osterior \\\\\\\\ from\\\\ inferior\\\\ towards\\\\
\\\\textbf{S}uperior \\\\end{Bmatrix} \\\\]

The output orientation is specified with
SetDesiredCoordinateOrientation. The input coordinate orientation is
computed from the input image's direction cosine matrix.
See:
 itk::simple::DICOMOrient for the procedural interface

 itk::DICOMOrientImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDICOMOrientImageFilter.h
";

%feature("docstring")  itk::simple::DICOMOrientImageFilter::DICOMOrientImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DICOMOrientImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DICOMOrientImageFilter::GetDesiredCoordinateOrientation "

Set/Get the desired coordinate orientation for the output image

";

%feature("docstring")  itk::simple::DICOMOrientImageFilter::GetFlipAxes "

Get flip axes.

This value is computed during Update.    This is a measurement. Its value is updated in the Execute
methods, so the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::DICOMOrientImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DICOMOrientImageFilter::GetPermuteOrder "

Get axes permute order.

This value is computed during Update.    This is a measurement. Its value is updated in the Execute
methods, so the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::DICOMOrientImageFilter::SetDesiredCoordinateOrientation "

Set/Get the desired coordinate orientation for the output image

";

%feature("docstring")  itk::simple::DICOMOrientImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DICOMOrientImageFilter::~DICOMOrientImageFilter "

Destructor

";


%feature("docstring") itk::simple::DanielssonDistanceMapImageFilter "

This filter computes the distance map of the input image as an
approximation with pixel accuracy to the Euclidean distance.


TInputImage

Input Image Type

TOutputImage

Output Image Type

TVoronoiImage

Voronoi Image Type. Note the default value is TInputImage.

The input is assumed to contain numeric codes defining objects. The
filter will produce as output the following images:


A Voronoi partition using the same numeric codes as the input.

A distance map with the approximation to the euclidean distance. from
a particular pixel to the nearest object to this pixel in the input
image.

A vector map containing the component of the vector relating the
current pixel with the closest point of the closest object to this
pixel. Given that the components of the distance are computed in
\"pixels\", the vector is represented by an itk::Offset . That is, physical coordinates are not used.
 This filter is N-dimensional and known to be efficient in
computational time. The algorithm is the N-dimensional version of the
4SED algorithm given for two dimensions in:

Danielsson, Per-Erik. Euclidean Distance Mapping. Computer Graphics
and Image Processing 14, 227-248 (1980).
See:
 itk::simple::DanielssonDistanceMap for the procedural interface

 itk::DanielssonDistanceMapImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDanielssonDistanceMapImageFilter.h
";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::DanielssonDistanceMapImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::GetInputIsBinary "

Get if the input is binary. See SetInputIsBinary() .

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::GetSquaredDistance "

Get the distance squared.

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::GetUseImageSpacing "

Get whether spacing is used.

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::InputIsBinaryOff "
";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::InputIsBinaryOn "

Set the value of InputIsBinary to true or false respectfully.

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::SetInputIsBinary "

Set if the input is binary. If this variable is set, each nonzero
pixel in the input image will be given a unique numeric code to be
used by the Voronoi partition. If the image is binary but you are not
interested in the Voronoi regions of the different nonzero pixels,
then you need not set this.

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::SetSquaredDistance "

Set if the distance should be squared.

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::SetUseImageSpacing "

Set if image spacing should be used in computing distances.

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::SquaredDistanceOff "
";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::SquaredDistanceOn "

Set the value of SquaredDistance to true or false respectfully.

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::DanielssonDistanceMapImageFilter::~DanielssonDistanceMapImageFilter "

Destructor

";


%feature("docstring") itk::simple::DemonsRegistrationFilter "

Deformably register two images using the demons algorithm.


DemonsRegistrationFilter implements the demons deformable algorithm that register two images
by computing the displacement field which will map a moving image onto
a fixed image.

A displacement field is represented as a image whose pixel type is
some vector type with at least N elements, where N is the dimension of
the fixed image. The vector type must support element access via
operator []. It is assumed that the vector elements behave like
floating point scalars.

This class is templated over the fixed image type, moving image type
and the displacement field type.

The input fixed and moving images are set via methods SetFixedImage
and SetMovingImage respectively. An initial displacement field maybe
set via SetInitialDisplacementField or SetInput. If no initial field
is set, a zero field is used as the initial condition.

The algorithm has one parameters: the number of iteration to be
performed.

The output displacement field can be obtained via methods GetOutput or
GetDisplacementField.

This class make use of the finite difference solver hierarchy. Update
for each iteration is computed in DemonsRegistrationFunction .


WARNING:
This filter assumes that the fixed image type, moving image type and
displacement field type all have the same number of dimensions.

See:
 DemonsRegistrationFunction

 itk::DemonsRegistrationFilter for the Doxygen on the original ITK class.


C++ includes: sitkDemonsRegistrationFilter.h
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::DemonsRegistrationFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::Execute "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetElapsedIterations "

Number of iterations run.


This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetIntensityDifferenceThreshold "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetMetric "

Get the metric value. The metric value is the mean square difference
in intensity between the fixed image and transforming moving image
computed over the the overlapping region between the two images. This
is value is only available for the previous iteration and NOT the
current iteration.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetRMSChange "

The Root Mean Square of the levelset upon termination.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetUseImageSpacing "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::GetUseMovingImageGradient "

Switch between using the fixed image and moving image gradient for
computing the displacement field updates.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetIntensityDifferenceThreshold "

Set/Get the threshold below which the absolute difference of intensity
yields a match. When the intensities match between a moving and fixed
image pixel, the update vector (for that iteration) will be the zero
vector. Default is 0.001.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetStandardDeviations "

Set the values of the StandardDeviations vector all to value

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the values of the UpdateFieldStandardDeviations vector all to
value

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetUseImageSpacing "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SetUseMovingImageGradient "

Switch between using the fixed image and moving image gradient for
computing the displacement field updates.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SmoothDisplacementFieldOff "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SmoothDisplacementFieldOn "

Set the value of SmoothDisplacementField to true or false
respectfully.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SmoothUpdateFieldOff "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::SmoothUpdateFieldOn "

Set the value of SmoothUpdateField to true or false respectfully.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::UseMovingImageGradientOff "
";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::UseMovingImageGradientOn "

Set the value of UseMovingImageGradient to true or false respectfully.

";

%feature("docstring")  itk::simple::DemonsRegistrationFilter::~DemonsRegistrationFilter "

Destructor

";


%feature("docstring") itk::simple::DerivativeImageFilter "

Computes the directional derivative of an image. The directional
derivative at each pixel location is computed by convolution with a
derivative operator of user-specified order.


SetOrder specifies the order of the derivative.

SetDirection specifies the direction of the derivative with respect to
the coordinate axes of the image.


See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::Derivative for the procedural interface

 itk::DerivativeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDerivativeImageFilter.h
";

%feature("docstring")  itk::simple::DerivativeImageFilter::DerivativeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DerivativeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DerivativeImageFilter::GetDirection "

The output pixel type must be signed. Standard get/set macros for
filter parameters.

";

%feature("docstring")  itk::simple::DerivativeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DerivativeImageFilter::GetOrder "

The output pixel type must be signed. Standard get/set macros for
filter parameters.

";

%feature("docstring")  itk::simple::DerivativeImageFilter::GetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::DerivativeImageFilter::SetDirection "

The output pixel type must be signed. Standard get/set macros for
filter parameters.

";

%feature("docstring")  itk::simple::DerivativeImageFilter::SetOrder "

The output pixel type must be signed. Standard get/set macros for
filter parameters.

";

%feature("docstring")  itk::simple::DerivativeImageFilter::SetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::DerivativeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DerivativeImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::DerivativeImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::DerivativeImageFilter::~DerivativeImageFilter "

Destructor

";


%feature("docstring") itk::simple::DiffeomorphicDemonsRegistrationFilter "

Deformably register two images using a diffeomorphic demons algorithm.


This class was contributed by Tom Vercauteren, INRIA & Mauna Kea
Technologies, based on a variation of the DemonsRegistrationFilter . The basic modification is to use diffeomorphism exponentials.

See T. Vercauteren, X. Pennec, A. Perchant and N. Ayache, \"Non-
parametric Diffeomorphic Image Registration with the Demons
Algorithm\", Proc. of MICCAI 2007.

DiffeomorphicDemonsRegistrationFilter implements the demons deformable algorithm that register two images
by computing the deformation field which will map a moving image onto
a fixed image.

A deformation field is represented as a image whose pixel type is some
vector type with at least N elements, where N is the dimension of the
fixed image. The vector type must support element access via operator
[]. It is assumed that the vector elements behave like floating point
scalars.

This class is templated over the fixed image type, moving image type
and the deformation field type.

The input fixed and moving images are set via methods SetFixedImage
and SetMovingImage respectively. An initial deformation field maybe
set via SetInitialDisplacementField or SetInput. If no initial field
is set, a zero field is used as the initial condition.

The output deformation field can be obtained via methods GetOutput or
GetDisplacementField.

This class make use of the finite difference solver hierarchy. Update
for each iteration is computed in DemonsRegistrationFunction .


Tom Vercauteren, INRIA & Mauna Kea Technologies

WARNING:
This filter assumes that the fixed image type, moving image type and
deformation field type all have the same number of dimensions.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/510


See:
 DemonsRegistrationFilter

 DemonsRegistrationFunction

 itk::DiffeomorphicDemonsRegistrationFilter for the Doxygen on the original ITK class.


C++ includes: sitkDiffeomorphicDemonsRegistrationFilter.h
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::DiffeomorphicDemonsRegistrationFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::Execute "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetElapsedIterations "

Number of iterations run.


This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetIntensityDifferenceThreshold "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetMaximumUpdateStepLength "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetMetric "

Get the metric value. The metric value is the mean square difference
in intensity between the fixed image and transforming moving image
computed over the the overlapping region between the two images. This
value is calculated for the current iteration

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetRMSChange "

Set/Get the root mean squared change of the previous iteration. May
not be used by all solvers.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetUseFirstOrderExp "

Use a first-order approximation of the exponential. This amounts to
using an update rule of the type s <- s o (Id + u) instead of s <- s o
exp(u)

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetUseGradientType "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::GetUseImageSpacing "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetIntensityDifferenceThreshold "

Set/Get the threshold below which the absolute difference of intensity
yields a match. When the intensities match between a moving and fixed
image pixel, the update vector (for that iteration) will be the zero
vector. Default is 0.001.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetMaximumUpdateStepLength "

Set/Get the maximum length in terms of pixels of the vectors in the
update buffer.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetStandardDeviations "

Set the values of the StandardDeviations vector all to value

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the values of the UpdateFieldStandardDeviations vector all to
value

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetUseFirstOrderExp "

Use a first-order approximation of the exponential. This amounts to
using an update rule of the type s <- s o (Id + u) instead of s <- s o
exp(u)

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetUseGradientType "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SetUseImageSpacing "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SmoothDisplacementFieldOff "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SmoothDisplacementFieldOn "

Set the value of SmoothDisplacementField to true or false
respectfully.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SmoothUpdateFieldOff "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::SmoothUpdateFieldOn "

Set the value of SmoothUpdateField to true or false respectfully.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::UseFirstOrderExpOff "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::UseFirstOrderExpOn "

Set the value of UseFirstOrderExp to true or false respectfully.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::DiffeomorphicDemonsRegistrationFilter::~DiffeomorphicDemonsRegistrationFilter "

Destructor

";


%feature("docstring") itk::simple::DilateObjectMorphologyImageFilter "

dilation of an object in an image


Dilate an image using binary morphology. Pixel values matching the
object value are considered the \"foreground\" and all other pixels
are \"background\". This is useful in processing mask images
containing only one object.

If a pixel's value is equal to the object value and the pixel is
adjacent to a non-object valued pixel, then the kernel is centered on
the object-value pixel and neighboring pixels covered by the kernel
are assigned the object value. The structuring element is assumed to
be composed of binary values (zero or one).


See:
 ObjectMorphologyImageFilter , ErodeObjectMorphologyImageFilter

 BinaryDilateImageFilter

 itk::simple::DilateObjectMorphology for the procedural interface

 itk::DilateObjectMorphologyImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDilateObjectMorphologyImageFilter.h
";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::DilateObjectMorphologyImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::GetObjectValue "
";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::SetObjectValue "
";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DilateObjectMorphologyImageFilter::~DilateObjectMorphologyImageFilter "

Destructor

";


%feature("docstring") itk::simple::DiscreteGaussianDerivativeImageFilter "

Calculates image derivatives using discrete derivative gaussian
kernels. This filter calculates Gaussian derivative by separable
convolution of an image and a discrete Gaussian derivative operator
(kernel).


The Gaussian operators used here were described by Tony Lindeberg
(Discrete Scale-Space Theory and the Scale-Space Primal Sketch.
Dissertation. Royal Institute of Technology, Stockholm, Sweden. May
1991.)

The variance or standard deviation (sigma) will be evaluated as pixel
units if SetUseImageSpacing is off (false) or as physical units if
SetUseImageSpacing is on (true, default). The variance can be set
independently in each dimension.

When the Gaussian kernel is small, this filter tends to run faster
than itk::RecursiveGaussianImageFilter .


Ivan Macia, VICOMTech, Spain, http://www.vicomtech.es
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/1290


See:
 GaussianDerivativeOperator

 Image

 Neighborhood

 NeighborhoodOperator

 itk::simple::DiscreteGaussianDerivative for the procedural interface

 itk::DiscreteGaussianDerivativeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDiscreteGaussianDerivativeImageFilter.h
";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::DiscreteGaussianDerivativeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::GetMaximumError "

The algorithm will size the discrete kernel so that the error
resulting from truncation of the kernel is no greater than
MaximumError. The default is 0.01 in each dimension.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::GetMaximumKernelWidth "

Set the kernel to be no wider than MaximumKernelWidth pixels, even if
MaximumError demands it. The default is 32 pixels.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::GetNormalizeAcrossScale "

Set/Get the flag for calculating scale-space normalized derivatives.
Normalized derivatives are obtained multiplying by the scale parameter
t.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::GetOrder "

Order of derivatives in each dimension. Sets the derivative order
independently for each dimension, but see also SetOrder(const unsigned int v) . The default is 1 in each dimension.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::GetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations. Default is ImageSpacingOn.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::GetVariance "

The variance for the discrete Gaussian kernel. Sets the variance
independently for each dimension, but see also SetVariance(const double v) . The default is 0.0 in each dimension. If UseImageSpacing is true,
the units are the physical units of your image. If UseImageSpacing is
false then the units are pixels.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::NormalizeAcrossScaleOff "
";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::NormalizeAcrossScaleOn "

Set the value of NormalizeAcrossScale to true or false respectfully.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::SetMaximumError "

Convenience Set methods for setting all dimensional parameters to the
same values.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::SetMaximumKernelWidth "

Set the kernel to be no wider than MaximumKernelWidth pixels, even if
MaximumError demands it. The default is 32 pixels.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::SetNormalizeAcrossScale "

Set/Get the flag for calculating scale-space normalized derivatives.
Normalized derivatives are obtained multiplying by the scale parameter
t.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::SetOrder "

Convenience Set methods for setting all dimensional parameters to the
same values.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::SetOrder "

Set the values of the Order vector all to value

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::SetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations. Default is ImageSpacingOn.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::SetVariance "

Convenience Set methods for setting all dimensional parameters to the
same values.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::SetVariance "

Set the values of the Variance vector all to value

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::DiscreteGaussianDerivativeImageFilter::~DiscreteGaussianDerivativeImageFilter "

Destructor

";


%feature("docstring") itk::simple::DiscreteGaussianImageFilter "

Blurs an image by separable convolution with discrete gaussian
kernels. This filter performs Gaussian blurring by separable
convolution of an image and a discrete Gaussian operator (kernel).


The Gaussian operator used here was described by Tony Lindeberg
(Discrete Scale-Space Theory and the Scale-Space Primal Sketch.
Dissertation. Royal Institute of Technology, Stockholm, Sweden. May
1991.) The Gaussian kernel used here was designed so that smoothing
and derivative operations commute after discretization.

The variance or standard deviation (sigma) will be evaluated as pixel
units if SetUseImageSpacing is off (false) or as physical units if
SetUseImageSpacing is on (true, default). The variance can be set
independently in each dimension.

When the Gaussian kernel is small, this filter tends to run faster
than itk::RecursiveGaussianImageFilter .


See:
 GaussianOperator

 Image

 Neighborhood

 NeighborhoodOperator

 RecursiveGaussianImageFilter

 itk::simple::DiscreteGaussian for the procedural interface

 itk::DiscreteGaussianImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDiscreteGaussianImageFilter.h
";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::DiscreteGaussianImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::GetMaximumError "

The algorithm will size the discrete kernel so that the error
resulting from truncation of the kernel is no greater than
MaximumError. The default is 0.01 in each dimension.

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::GetMaximumKernelWidth "

Set the kernel to be no wider than MaximumKernelWidth pixels, even if
MaximumError demands it. The default is 32 pixels.

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::GetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::GetVariance "

The variance for the discrete Gaussian kernel. Sets the variance
independently for each dimension, but see also SetVariance(const double v) . The default is 0.0 in each dimension. If UseImageSpacing is true,
the units are the physical units of your image. If UseImageSpacing is
false then the units are pixels.

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::SetMaximumError "
";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::SetMaximumError "

Set the values of the MaximumError vector all to value

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::SetMaximumKernelWidth "

Set the kernel to be no wider than MaximumKernelWidth pixels, even if
MaximumError demands it. The default is 32 pixels.

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::SetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::SetVariance "
";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::SetVariance "

Set the values of the Variance vector all to value

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::DiscreteGaussianImageFilter::~DiscreteGaussianImageFilter "

Destructor

";


%feature("docstring") itk::simple::DisplacementFieldJacobianDeterminantFilter "

Computes a scalar image from a vector image (e.g., deformation field)
input, where each output scalar at each pixel is the Jacobian
determinant of the vector field at that location. This calculation is
correct in the case where the vector image is a \"displacement\" from
the current location. The computation for the jacobian determinant is:
det[ dT/dx ] = det[ I + du/dx ].


Overview
This filter is based on itkVectorGradientMagnitudeImageFilter and
supports the m_DerivativeWeights weights for partial derivatives.
 Note that the determinant of a zero vector field is also zero,
whereas the Jacobian determinant of the corresponding identity warp
transformation is 1.0. In order to compute the effective deformation
Jacobian determinant 1.0 must be added to the diagonal elements of
Jacobian prior to taking the derivative. i.e. det([ (1.0+dx/dx) dx/dy
dx/dz ; dy/dx (1.0+dy/dy) dy/dz; dz/dx dz/dy (1.0+dz/dz) ])

Template Parameters (Input and Output)
This filter has one required template parameter which defines the
input image type. The pixel type of the input image is assumed to be a
vector (e.g., itk::Vector , itk::RGBPixel , itk::FixedArray ). The scalar type of the vector components must be castable to
floating point. Instantiating with an image of RGBPixel<unsigned
short>, for example, is allowed, but the filter will convert it to an
image of Vector<float,3> for processing.
 The second template parameter, TRealType, can be optionally specified
to define the scalar numerical type used in calculations. This is the
component type of the output image, which will be of
itk::Vector<TRealType, N>, where N is the number of channels in the
multiple component input image. The default type of TRealType is
float. For extra precision, you may safely change this parameter to
double.

The third template parameter is the output image type. The third
parameter will be automatically constructed from the first and second
parameters, so it is not necessary (or advisable) to set this
parameter explicitly. Given an M-channel input image with
dimensionality N, and a numerical type specified as TRealType, the
output image will be of type itk::Image<TRealType, N>.

Filter Parameters
The method SetUseImageSpacingOn will cause derivatives in the image to
be scaled (inversely) with the pixel size of the input image,
effectively taking derivatives in world coordinates (versus isotropic
image space). SetUseImageSpacingOff turns this functionality off.
Default is UseImageSpacingOn. The parameter UseImageSpacing can be set
directly with the method SetUseImageSpacing(bool) .
 Weights can be applied to the derivatives directly using the
SetDerivativeWeights method. Note that if UseImageSpacing is set to
TRUE (ON), then these weights will be overridden by weights derived
from the image spacing when the filter is updated. The argument to
this method is a C array of TRealValue type.

Constraints
We use vnl_det for determinant computation, which only supports square
matrices. So the vector dimension of the input image values must be
equal to the image dimensions, which is trivially true for a
deformation field that maps an n-dimensional space onto itself.
 Currently, dimensions up to and including 4 are supported. This
limitation comes from the presence of vnl_det() functions for matrices
of dimension up to 4x4.

The template parameter TRealType must be floating point (float or
double) or a user-defined \"real\" numerical type with arithmetic
operations defined sufficient to compute derivatives.


See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

This class was adapted by

Hans J. Johnson, The University of Iowa from code provided by

Tom Vercauteren, INRIA & Mauna Kea Technologies

Torsten Rohlfing, Neuroscience Program, SRI International.

See:
 itk::simple::DisplacementFieldJacobianDeterminantFilter for the procedural interface

 itk::DisplacementFieldJacobianDeterminantFilter for the Doxygen on the original ITK class.


C++ includes: sitkDisplacementFieldJacobianDeterminantFilter.h
";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::DisplacementFieldJacobianDeterminantFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::GetDerivativeWeights "

Directly Set/Get the array of weights used in the gradient
calculations. Note that calling UseImageSpacingOn will clobber these
values.

";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::GetUseImageSpacing "
";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::SetDerivativeWeights "

Directly Set/Get the array of weights used in the gradient
calculations. Note that calling UseImageSpacingOn will clobber these
values.

";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::SetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::DisplacementFieldJacobianDeterminantFilter::~DisplacementFieldJacobianDeterminantFilter "

Destructor

";


%feature("docstring") itk::simple::DisplacementFieldTransform "

A dense deformable transform over a bounded spatial domain for 2D or
3D coordinates space.



See:
 itk::DisplacementFieldTransform


C++ includes: sitkDisplacementFieldTransform.h
";

%feature("docstring")  itk::simple::DisplacementFieldTransform::DisplacementFieldTransform "
";

%feature("docstring")  itk::simple::DisplacementFieldTransform::DisplacementFieldTransform "

Consume an image to construct a displacement field transform.



WARNING:
The input displacement image is transferred to the constructed
transform object. The input image is modified to be a default
constructed Image object.
Image must be of sitkVectorFloat64 pixel type with the number of components
equal to the image dimension.

";

%feature("docstring")  itk::simple::DisplacementFieldTransform::DisplacementFieldTransform "
";

%feature("docstring")  itk::simple::DisplacementFieldTransform::DisplacementFieldTransform "
";

%feature("docstring")  itk::simple::DisplacementFieldTransform::GetDisplacementField "

Todo
The returned image should not directly modify the internal
displacement field.


";

%feature("docstring")  itk::simple::DisplacementFieldTransform::GetInverseDisplacementField "

Todo
The returned image is should not directly modify the internal
displacement field.


";

%feature("docstring")  itk::simple::DisplacementFieldTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DisplacementFieldTransform::SetDisplacementField "

Consume an image, and set the displacement field.


parameters
WARNING:
The ownership of the input displacement image is transferred to the
constructed transform object. The input image is modified to be a
default constructed Image object.
Image must be of sitkVectorFloat64 pixel type with the number of components
equal to the image dimension.

";

%feature("docstring")  itk::simple::DisplacementFieldTransform::SetInterpolator "

Set the interpolator used between the field voxels.

";

%feature("docstring")  itk::simple::DisplacementFieldTransform::SetInverseDisplacementField "

fixed parameter

";

%feature("docstring")  itk::simple::DisplacementFieldTransform::SetSmoothingBSplineOnUpdate "
";

%feature("docstring")  itk::simple::DisplacementFieldTransform::SetSmoothingGaussianOnUpdate "
";

%feature("docstring")  itk::simple::DisplacementFieldTransform::SetSmoothingOff "
";

%feature("docstring")  itk::simple::DisplacementFieldTransform::~DisplacementFieldTransform "
";


%feature("docstring") itk::simple::DivideFloorImageFilter "

Implements pixel-wise generic operation of two images, or of an image
and a constant.


This class is parameterized over the types of the two input images and
the type of the output image. It is also parameterized by the
operation to be applied. A Functor style is used.

The constant must be of the same type than the pixel type of the
corresponding image. It is wrapped in a SimpleDataObjectDecorator so it can be updated through the pipeline. The SetConstant() and
GetConstant() methods are provided as shortcuts to set or get the
constant value without manipulating the decorator.


See:
 BinaryGeneratorImagFilter

 UnaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::DivideFloor for the procedural interface

 itk::BinaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDivideFloorImageFilter.h
";

%feature("docstring")  itk::simple::DivideFloorImageFilter::DivideFloorImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DivideFloorImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::DivideFloorImageFilter::Execute "
";

%feature("docstring")  itk::simple::DivideFloorImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::DivideFloorImageFilter::Execute "
";

%feature("docstring")  itk::simple::DivideFloorImageFilter::Execute "
";

%feature("docstring")  itk::simple::DivideFloorImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DivideFloorImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DivideFloorImageFilter::~DivideFloorImageFilter "

Destructor

";


%feature("docstring") itk::simple::DivideImageFilter "

Pixel-wise division of two images.


This class is templated over the types of the two input images and the
type of the output image. When the divisor is zero, the division
result is set to the maximum number that can be represented by default
to avoid exception. Numeric conversions (castings) are done by the C++
defaults.
See:
 itk::simple::Divide for the procedural interface

 itk::DivideImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDivideImageFilter.h
";

%feature("docstring")  itk::simple::DivideImageFilter::DivideImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DivideImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::DivideImageFilter::Execute "
";

%feature("docstring")  itk::simple::DivideImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::DivideImageFilter::Execute "
";

%feature("docstring")  itk::simple::DivideImageFilter::Execute "
";

%feature("docstring")  itk::simple::DivideImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DivideImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DivideImageFilter::~DivideImageFilter "

Destructor

";


%feature("docstring") itk::simple::DivideRealImageFilter "

Implements pixel-wise generic operation of two images, or of an image
and a constant.


This class is parameterized over the types of the two input images and
the type of the output image. It is also parameterized by the
operation to be applied. A Functor style is used.

The constant must be of the same type than the pixel type of the
corresponding image. It is wrapped in a SimpleDataObjectDecorator so it can be updated through the pipeline. The SetConstant() and
GetConstant() methods are provided as shortcuts to set or get the
constant value without manipulating the decorator.


See:
 BinaryGeneratorImagFilter

 UnaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::DivideReal for the procedural interface

 itk::BinaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDivideRealImageFilter.h
";

%feature("docstring")  itk::simple::DivideRealImageFilter::DivideRealImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DivideRealImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::DivideRealImageFilter::Execute "
";

%feature("docstring")  itk::simple::DivideRealImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::DivideRealImageFilter::Execute "
";

%feature("docstring")  itk::simple::DivideRealImageFilter::Execute "
";

%feature("docstring")  itk::simple::DivideRealImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DivideRealImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DivideRealImageFilter::~DivideRealImageFilter "

Destructor

";


%feature("docstring") itk::simple::DoubleThresholdImageFilter "

Binarize an input image using double thresholding.


Double threshold addresses the difficulty in selecting a threshold
that will select the objects of interest without selecting extraneous
objects. Double threshold considers two threshold ranges: a narrow
range and a wide range (where the wide range encompasses the narrow
range). If the wide range was used for a traditional threshold (where
values inside the range map to the foreground and values outside the
range map to the background), many extraneous pixels may survive the
threshold operation. If the narrow range was used for a traditional
threshold, then too few pixels may survive the threshold.

Double threshold uses the narrow threshold image as a marker image and
the wide threshold image as a mask image in the geodesic dilation.
Essentially, the marker image (narrow threshold) is dilated but
constrained to lie within the mask image (wide threshold). Thus, only
the objects of interest (those pixels that survived the narrow
threshold) are extracted but the those objects appear in the final
image as they would have if the wide threshold was used.


See:
 GrayscaleGeodesicDilateImageFilter

 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::DoubleThreshold for the procedural interface

 itk::DoubleThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkDoubleThresholdImageFilter.h
";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::DoubleThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::GetThreshold1 "

Get the threshold values.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::GetThreshold2 "

Get the threshold values.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::GetThreshold3 "

Get the threshold values.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::GetThreshold4 "

Get the threshold values.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value. The default value NumericTraits<OutputPixelType>::max()

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::ZeroValue() .

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::SetThreshold1 "

Set the thresholds. Four thresholds should be specified. The two lower
thresholds default to NumericTraits<InputPixelType>::NonpositiveMin() . The two upper thresholds default NumericTraits<InputPixelType>::max . Threshold1 <= Threshold2 <= Threshold3 <= Threshold4.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::SetThreshold2 "

Set the thresholds. Four thresholds should be specified. The two lower
thresholds default to NumericTraits<InputPixelType>::NonpositiveMin() . The two upper thresholds default NumericTraits<InputPixelType>::max . Threshold1 <= Threshold2 <= Threshold3 <= Threshold4.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::SetThreshold3 "

Set the thresholds. Four thresholds should be specified. The two lower
thresholds default to NumericTraits<InputPixelType>::NonpositiveMin() . The two upper thresholds default NumericTraits<InputPixelType>::max . Threshold1 <= Threshold2 <= Threshold3 <= Threshold4.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::SetThreshold4 "

Set the thresholds. Four thresholds should be specified. The two lower
thresholds default to NumericTraits<InputPixelType>::NonpositiveMin() . The two upper thresholds default NumericTraits<InputPixelType>::max . Threshold1 <= Threshold2 <= Threshold3 <= Threshold4.

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::DoubleThresholdImageFilter::~DoubleThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::EdgePotentialImageFilter "

Computes the edge potential of an image from the image gradient.


Input to this filter should be a CovariantVector image representing the image gradient.

The filter expect both the input and output images to have the same
number of dimensions, and the output to be of a scalar image type.
See:
 itk::simple::EdgePotential for the procedural interface

 itk::EdgePotentialImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkEdgePotentialImageFilter.h
";

%feature("docstring")  itk::simple::EdgePotentialImageFilter::EdgePotentialImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::EdgePotentialImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::EdgePotentialImageFilter::Execute "
";

%feature("docstring")  itk::simple::EdgePotentialImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::EdgePotentialImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::EdgePotentialImageFilter::~EdgePotentialImageFilter "

Destructor

";


%feature("docstring") itk::simple::EqualImageFilter "

Implements pixel-wise generic operation of two images, or of an image
and a constant.


This class is parameterized over the types of the two input images and
the type of the output image. It is also parameterized by the
operation to be applied. A Functor style is used.

The constant must be of the same type than the pixel type of the
corresponding image. It is wrapped in a SimpleDataObjectDecorator so it can be updated through the pipeline. The SetConstant() and
GetConstant() methods are provided as shortcuts to set or get the
constant value without manipulating the decorator.


See:
 BinaryGeneratorImagFilter

 UnaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::Equal for the procedural interface

 itk::BinaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkEqualImageFilter.h
";

%feature("docstring")  itk::simple::EqualImageFilter::EqualImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::EqualImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::EqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::EqualImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::EqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::EqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::EqualImageFilter::Execute "

Execute the filter on an image and a constant with the given
parameters

";

%feature("docstring")  itk::simple::EqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::EqualImageFilter::GetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::EqualImageFilter::GetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::EqualImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::EqualImageFilter::SetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::EqualImageFilter::SetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::EqualImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::EqualImageFilter::~EqualImageFilter "

Destructor

";


%feature("docstring") itk::simple::ErodeObjectMorphologyImageFilter "

Erosion of an object in an image.


Erosion of an image using binary morphology. Pixel values matching the
object value are considered the \"object\" and all other pixels are
\"background\". This is useful in processing mask images containing
only one object.

If the pixel covered by the center of the kernel has the pixel value
ObjectValue and the pixel is adjacent to a non-object valued pixel,
then the kernel is centered on the object-value pixel and neighboring
pixels covered by the kernel are assigned the background value. The
structuring element is assumed to be composed of binary values (zero
or one).


See:
 ObjectMorphologyImageFilter , BinaryFunctionErodeImageFilter

 BinaryErodeImageFilter

 itk::simple::ErodeObjectMorphology for the procedural interface

 itk::ErodeObjectMorphologyImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkErodeObjectMorphologyImageFilter.h
";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::ErodeObjectMorphologyImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::GetBackgroundValue "

Get the value to be assigned to eroded pixels

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::GetObjectValue "
";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::SetBackgroundValue "

Set the value to be assigned to eroded pixels

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::SetObjectValue "
";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ErodeObjectMorphologyImageFilter::~ErodeObjectMorphologyImageFilter "

Destructor

";


%feature("docstring") itk::simple::Euler2DTransform "

A rigid 2D transform with rotation in radians around a fixed center
with translation.



See:
 itk::Euler2DTransform


C++ includes: sitkEuler2DTransform.h
";

%feature("docstring")  itk::simple::Euler2DTransform::Euler2DTransform "
";

%feature("docstring")  itk::simple::Euler2DTransform::Euler2DTransform "
";

%feature("docstring")  itk::simple::Euler2DTransform::Euler2DTransform "
";

%feature("docstring")  itk::simple::Euler2DTransform::Euler2DTransform "
";

%feature("docstring")  itk::simple::Euler2DTransform::GetAngle "
";

%feature("docstring")  itk::simple::Euler2DTransform::GetCenter "
";

%feature("docstring")  itk::simple::Euler2DTransform::GetMatrix "

additional methods

";

%feature("docstring")  itk::simple::Euler2DTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::Euler2DTransform::GetTranslation "
";

%feature("docstring")  itk::simple::Euler2DTransform::SetAngle "

parameter

";

%feature("docstring")  itk::simple::Euler2DTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::Euler2DTransform::SetMatrix "
";

%feature("docstring")  itk::simple::Euler2DTransform::SetTranslation "
";

%feature("docstring")  itk::simple::Euler2DTransform::~Euler2DTransform "
";


%feature("docstring") itk::simple::Euler3DTransform "

A rigid 3D transform with rotation in radians around a fixed center
with translation.



See:
 itk::Euler3DTransform


C++ includes: sitkEuler3DTransform.h
";

%feature("docstring")  itk::simple::Euler3DTransform::ComputeZYXOff "
";

%feature("docstring")  itk::simple::Euler3DTransform::ComputeZYXOn "
";

%feature("docstring")  itk::simple::Euler3DTransform::Euler3DTransform "
";

%feature("docstring")  itk::simple::Euler3DTransform::Euler3DTransform "
";

%feature("docstring")  itk::simple::Euler3DTransform::Euler3DTransform "
";

%feature("docstring")  itk::simple::Euler3DTransform::Euler3DTransform "
";

%feature("docstring")  itk::simple::Euler3DTransform::GetAngleX "
";

%feature("docstring")  itk::simple::Euler3DTransform::GetAngleY "
";

%feature("docstring")  itk::simple::Euler3DTransform::GetAngleZ "
";

%feature("docstring")  itk::simple::Euler3DTransform::GetCenter "
";

%feature("docstring")  itk::simple::Euler3DTransform::GetComputeZYX "
";

%feature("docstring")  itk::simple::Euler3DTransform::GetMatrix "

additional methods

";

%feature("docstring")  itk::simple::Euler3DTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::Euler3DTransform::GetTranslation "
";

%feature("docstring")  itk::simple::Euler3DTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::Euler3DTransform::SetComputeZYX "
";

%feature("docstring")  itk::simple::Euler3DTransform::SetMatrix "
";

%feature("docstring")  itk::simple::Euler3DTransform::SetRotation "

parameter

";

%feature("docstring")  itk::simple::Euler3DTransform::SetTranslation "
";

%feature("docstring")  itk::simple::Euler3DTransform::~Euler3DTransform "
";


%feature("docstring") itk::simple::ExpImageFilter "

Computes the exponential function of each pixel.


The computation is performed using std::exp(x).
See:
 itk::simple::Exp for the procedural interface

 itk::ExpImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkExpImageFilter.h
";

%feature("docstring")  itk::simple::ExpImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ExpImageFilter::Execute "
";

%feature("docstring")  itk::simple::ExpImageFilter::ExpImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ExpImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ExpImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ExpImageFilter::~ExpImageFilter "

Destructor

";


%feature("docstring") itk::simple::ExpNegativeImageFilter "

Computes the function exp(-K.x) for each input pixel.


Every output pixel is equal to std::exp(-K.x ). where x is the
intensity of the homologous input pixel, and K is a user-provided
constant.
See:
 itk::simple::ExpNegative for the procedural interface

 itk::ExpNegativeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkExpNegativeImageFilter.h
";

%feature("docstring")  itk::simple::ExpNegativeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ExpNegativeImageFilter::Execute "
";

%feature("docstring")  itk::simple::ExpNegativeImageFilter::ExpNegativeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ExpNegativeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ExpNegativeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ExpNegativeImageFilter::~ExpNegativeImageFilter "

Destructor

";


%feature("docstring") itk::simple::ExpandImageFilter "

Expand the size of an image by an integer factor in each dimension.


ExpandImageFilter increases the size of an image by an integer factor in each dimension
using a interpolation method. The output image size in each dimension
is given by:

OutputSize[j] = InputSize[j] * ExpandFactors[j]

The output values are obtained by interpolating the input image. The
default interpolation type used is the LinearInterpolateImageFunction . The user can specify a particular interpolation function via SetInterpolator() . Note that the input interpolator must derive from base class InterpolateImageFunction .

This filter will produce an output with different pixel spacing that
its input image such that:

OutputSpacing[j] = InputSpacing[j] / ExpandFactors[j]

The filter is templated over the input image type and the output image
type.

This filter is implemented as a multithreaded filter and supports
streaming.

This filter assumes that the input and output image has the same
number of dimensions.


See:
 InterpolateImageFunction

 LinearInterpolationImageFunction

 itk::simple::Expand for the procedural interface

 itk::ExpandImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkExpandImageFilter.h
";

%feature("docstring")  itk::simple::ExpandImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ExpandImageFilter::ExpandImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ExpandImageFilter::GetExpandFactors "

Get the expand factors.

";

%feature("docstring")  itk::simple::ExpandImageFilter::GetInterpolator "

Get/Set the interpolator function.

";

%feature("docstring")  itk::simple::ExpandImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ExpandImageFilter::SetExpandFactor "

Custom public declarations

";

%feature("docstring")  itk::simple::ExpandImageFilter::SetExpandFactors "

Set the expand factors. Values are clamped to a minimum value of 1.
Default is 1 for all dimensions.

";

%feature("docstring")  itk::simple::ExpandImageFilter::SetExpandFactors "

Set the values of the ExpandFactors vector all to value

";

%feature("docstring")  itk::simple::ExpandImageFilter::SetInterpolator "

Get/Set the interpolator function.

";

%feature("docstring")  itk::simple::ExpandImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ExpandImageFilter::~ExpandImageFilter "

Destructor

";


%feature("docstring") itk::simple::ExtractImageFilter "

Decrease the image size by cropping the image to the selected region
bounds.


ExtractImageFilter changes the image boundary of an image by removing pixels outside the
target region. The region is specified as a Size and Index. The Size must be specified, while the Index defaults to zeros.

ExtractImageFilter can collapses dimensions so that the input image may have more
dimensions than the output image (i.e. 4-D input image to a 3-D output
image). To specify what dimensions to collapse, the Size must be specified. For any dimension dim where the Size[dim] == 0, that dimension is collapsed. The index to collapse on is
specified by Index[dim]. For example, we have a image 4D = a 4x4x4x4 image, and we want
to get a 3D image, 3D = a 4x4x4 image, specified as [x,y,z,2] from 4D
(i.e. the 3rd \"time\" slice from 4D). The Size = [4,4,4,0] and Index = [0,0,0,2].

The number of dimension in Size and Index must at least dimension of the input image. The number of non-zero
dimensions in Size determines the output dimension.

Determining the direction of the collapsed output image from an larger
dimensional input space is an ill defined problem in general. It is
required that the application developer select the desired
transformation strategy for collapsing direction cosines. The strategy
defaults to the guess approach. Direction Collapsing Strategies: 1)
DirectionCollapseToUnknown(); This is the default in ITK and the
filter can not run when this is set. 1) DirectionCollapseToIdentity();
Output has identity direction no matter what 2)
DirectionCollapseToSubmatrix(); Output direction is the sub-matrix if
it is positive definite, else throw an exception.

This filter is implemented as a multithreaded filter. It provides a
DynamicThreadedGenerateData() method for its implementation.


See:
 CropImageFilter

 itk::simple::Extract for the procedural interface

 itk::ExtractImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkExtractImageFilter.h
";

%feature("docstring")  itk::simple::ExtractImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ExtractImageFilter::Execute "
";

%feature("docstring")  itk::simple::ExtractImageFilter::ExtractImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ExtractImageFilter::GetDirectionCollapseToStrategy "

Get the currently set strategy for collapsing directions of physical
space.

";

%feature("docstring")  itk::simple::ExtractImageFilter::GetIndex "

Get the starting index to extract.

";

%feature("docstring")  itk::simple::ExtractImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ExtractImageFilter::GetSize "

Get the size of the region to extract.

";

%feature("docstring")  itk::simple::ExtractImageFilter::SetDirectionCollapseToStrategy "

Set the strategy to be used to collapse physical space dimensions.


DIRECTIONCOLLAPSETOIDENTITY Set the strategy so that all collapsed
images have an identity direction. Use this strategy when you know
that retention of the physical space orientation of the collapsed
image is not important.

DIRECTIONCOLLAPSETOGUESS Set the strategy so that all collapsed images
where output direction is the sub-matrix if it is positive definite,
else return identity. This is backwards compatible with ITKv3, but is
highly discouraged because the results are difficult to anticipate
under differing data scenarios.

DIRECTIONCOLLAPSETOSUBMATRIX Set the strategy so that all collapsed
images where output direction is the sub-matrix if it is positive
definite, else throw an exception. Use this strategy when it is known
that properly identified physical space sub-volumes can be reliably
extracted from a higher dimensional space. For example when the
application programmer knows that a 4D image is 3D+time, and that the
3D sub-space is properly defined.

";

%feature("docstring")  itk::simple::ExtractImageFilter::SetIndex "

Set the starting index of the input image to extract.


The index defaults to all zeros.

";

%feature("docstring")  itk::simple::ExtractImageFilter::SetSize "

Set the size of the region to extract.


The size of the region to extract should be specified. Dimensions
which have a size of 0 are collapsed. The number of non-zero sized
determines the output dimension.

";

%feature("docstring")  itk::simple::ExtractImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ExtractImageFilter::~ExtractImageFilter "

Destructor

";


%feature("docstring") itk::simple::FFTConvolutionImageFilter "

Convolve a given image with an arbitrary image kernel using
multiplication in the Fourier domain.


This filter produces output equivalent to the output of the ConvolutionImageFilter . However, it takes advantage of the convolution theorem to
accelerate the convolution computation when the kernel is large.


WARNING:
This filter ignores the spacing, origin, and orientation of the kernel
image and treats them as identical to those in the input image.
 This code was adapted from the Insight Journal contribution:

\"FFT Based Convolution\" by Gaetan Lehmann https://hdl.handle.net/10380/3154


See:
 ConvolutionImageFilter

 itk::simple::FFTConvolution for the procedural interface

 itk::FFTConvolutionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkFFTConvolutionImageFilter.h
";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::FFTConvolutionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::GetBoundaryCondition "
";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::GetNormalize "
";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::GetOutputRegionMode "
";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::NormalizeOff "
";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::NormalizeOn "

Set the value of Normalize to true or false respectfully.

";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::SetBoundaryCondition "
";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::SetNormalize "

Normalize the output image by the sum of the kernel components

";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::SetOutputRegionMode "
";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FFTConvolutionImageFilter::~FFTConvolutionImageFilter "

Destructor

";


%feature("docstring") itk::simple::FFTNormalizedCorrelationImageFilter "

Calculate normalized cross correlation using FFTs.


This filter calculates the normalized cross correlation (NCC) of two
images using FFTs instead of spatial correlation. It is much faster
than spatial correlation for reasonably large structuring elements.
This filter is a subclass of the more general MaskedFFTNormalizedCorrelationImageFilter and operates by essentially setting the masks in that algorithm to
images of ones. As described in detail in the references below, there
is no computational overhead to utilizing the more general masked
algorithm because the FFTs of the images of ones are still necessary
for the computations.

Inputs: Two images are required as inputs, fixedImage and movingImage.
In the context of correlation, inputs are often defined as: \"image\"
and \"template\". In this filter, the fixedImage plays the role of the
image, and the movingImage plays the role of the template. However,
this filter is capable of correlating any two images and is not
restricted to small movingImages (templates).

Optional parameters: The RequiredNumberOfOverlappingPixels enables the
user to specify how many voxels of the two images must overlap; any
location in the correlation map that results from fewer than this
number of voxels will be set to zero. Larger values zero-out pixels on
a larger border around the correlation image. Thus, larger values
remove less stable computations but also limit the capture range. If
RequiredNumberOfOverlappingPixels is set to 0, the default, no zeroing
will take place.

Image size: fixedImage and movingImage need not be the same size.
Furthermore, whereas some algorithms require that the \"template\" be
smaller than the \"image\" because of errors in the regions where the
two are not fully overlapping, this filter has no such restriction.

Image spacing: Since the computations are done in the pixel domain, all
input images must have the same spacing.

Outputs; The output is an image of RealPixelType that is the NCC of
the two images and its values range from -1.0 to 1.0. The size of this
NCC image is, by definition, size(fixedImage) + size(movingImage) - 1.

Example filter usage:


WARNING:
The pixel type of the output image must be of real type (float or
double). ConceptChecking is used to enforce the output pixel type. You
will get a compilation error if the pixel type of the output image is
not float or double.
 References: 1) D. Padfield. \"Masked object registration in the
Fourier domain.\" Transactions on Image Processing. 2) D. Padfield. \"Masked FFT registration\". In Proc.
Computer Vision and Pattern Recognition, 2010.


: Dirk Padfield, GE Global Research, padfield@research.ge.com

See:
 itk::simple::FFTNormalizedCorrelation for the procedural interface

 itk::FFTNormalizedCorrelationImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkFFTNormalizedCorrelationImageFilter.h
";

%feature("docstring")  itk::simple::FFTNormalizedCorrelationImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::FFTNormalizedCorrelationImageFilter::FFTNormalizedCorrelationImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FFTNormalizedCorrelationImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FFTNormalizedCorrelationImageFilter::GetRequiredNumberOfOverlappingPixels "
";

%feature("docstring")  itk::simple::FFTNormalizedCorrelationImageFilter::SetRequiredNumberOfOverlappingPixels "
";

%feature("docstring")  itk::simple::FFTNormalizedCorrelationImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FFTNormalizedCorrelationImageFilter::~FFTNormalizedCorrelationImageFilter "

Destructor

";


%feature("docstring") itk::simple::FFTPadImageFilter "

Pad an image to make it suitable for an FFT transformation.


FFT filters usually requires a specific image size. The size is
decomposed in several prime factors, and the filter only supports
prime factors up to a maximum value. This filter automatically finds
the greatest prime factor required by the available implementation and
pads the input appropriately.

This code was adapted from the Insight Journal contribution:

\"FFT Based Convolution\" by Gaetan Lehmann https://hdl.handle.net/10380/3154


Gaetan Lehmann

See:
 FFTShiftImageFilter

 itk::simple::FFTPad for the procedural interface

 itk::FFTPadImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkFFTPadImageFilter.h
";

%feature("docstring")  itk::simple::FFTPadImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::FFTPadImageFilter::FFTPadImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FFTPadImageFilter::GetBoundaryCondition "
";

%feature("docstring")  itk::simple::FFTPadImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FFTPadImageFilter::GetSizeGreatestPrimeFactor "

Set/Get the greatest prime factor allowed on the size of the padded
image. The filter increase the size of the image to reach a size with
the greatest prime factor smaller or equal to the specified value. The
default value is 13, which is the greatest prime number for which the
FFT are precomputed in FFTW, and thus gives very good performance. A
greatest prime factor of 2 produce a size which is a power of 2, and
thus is suitable for vnl base fft filters. A greatest prime factor of
1 or less - typically 0 - disable the extra padding.

";

%feature("docstring")  itk::simple::FFTPadImageFilter::SetBoundaryCondition "
";

%feature("docstring")  itk::simple::FFTPadImageFilter::SetSizeGreatestPrimeFactor "

Set/Get the greatest prime factor allowed on the size of the padded
image. The filter increase the size of the image to reach a size with
the greatest prime factor smaller or equal to the specified value. The
default value is 13, which is the greatest prime number for which the
FFT are precomputed in FFTW, and thus gives very good performance. A
greatest prime factor of 2 produce a size which is a power of 2, and
thus is suitable for vnl base fft filters. A greatest prime factor of
1 or less - typically 0 - disable the extra padding.

";

%feature("docstring")  itk::simple::FFTPadImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FFTPadImageFilter::~FFTPadImageFilter "

Destructor

";


%feature("docstring") itk::simple::FFTShiftImageFilter "

Shift the zero-frequency components of a Fourier transform to the
center of the image.


The Fourier transform produces an image where the zero frequency
components are in the corner of the image, making it difficult to
understand. This filter shifts the component to the center of the
image.


For images with an odd-sized dimension, applying this filter twice
will not produce the same image as the original one without using
SetInverse(true) on one (and only one) of the two filters.
https://hdl.handle.net/1926/321


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ForwardFFTImageFilter , InverseFFTImageFilter

 itk::simple::FFTShift for the procedural interface

 itk::FFTShiftImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkFFTShiftImageFilter.h
";

%feature("docstring")  itk::simple::FFTShiftImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::FFTShiftImageFilter::FFTShiftImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FFTShiftImageFilter::GetInverse "

Set/Get whether the filter must invert the transform or not. This
option has no effect if none of the size of the input image is even,
but is required to restore the original image if at least one of the
dimensions has an odd size.

";

%feature("docstring")  itk::simple::FFTShiftImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FFTShiftImageFilter::InverseOff "
";

%feature("docstring")  itk::simple::FFTShiftImageFilter::InverseOn "

Set the value of Inverse to true or false respectfully.

";

%feature("docstring")  itk::simple::FFTShiftImageFilter::SetInverse "

Set/Get whether the filter must invert the transform or not. This
option has no effect if none of the size of the input image is even,
but is required to restore the original image if at least one of the
dimensions has an odd size.

";

%feature("docstring")  itk::simple::FFTShiftImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FFTShiftImageFilter::~FFTShiftImageFilter "

Destructor

";


%feature("docstring") itk::simple::FastApproximateRankImageFilter "

A separable rank filter.


Medians aren't separable, but if you want a large robust smoother to
be relatively quick then it is worthwhile pretending that they are.

This code was contributed in the Insight Journal paper: \"Efficient
implementation of kernel filtering\" by Beare R., Lehmann G https://hdl.handle.net/1926/555 http://www.insight-journal.org/browse/publication/160


Richard Beare

See:
 itk::simple::FastApproximateRank for the procedural interface

 itk::FastApproximateRankImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkFastApproximateRankImageFilter.h
";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::FastApproximateRankImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::GetRank "
";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::SetRank "
";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FastApproximateRankImageFilter::~FastApproximateRankImageFilter "

Destructor

";


%feature("docstring") itk::simple::FastMarchingBaseImageFilter "

Apply the Fast Marching method to solve an Eikonal equation on an
image.


The speed function can be specified as a speed image or a speed
constant. The speed image is set using the method SetInput(). If the
speed image is nullptr, a constant speed function is used and is
specified using method the SetSpeedConstant() .

If the speed function is constant and of value one, fast marching
results is an approximate distance function from the initial alive
points.

There are two ways to specify the output image information
(LargestPossibleRegion, Spacing, Origin):


it is copied directly from the input speed image

it is specified by the user. Default values are used if the user does
not specify all the information.
 The output information is computed as follows.

If the speed image is nullptr or if the OverrideOutputInformation is
set to true, the output information is set from user specified
parameters. These parameters can be specified using methods


FastMarchingImageFilterBase::SetOutputRegion() ,

FastMarchingImageFilterBase::SetOutputSpacing() ,

FastMarchingImageFilterBase::SetOutputDirection() ,

FastMarchingImageFilterBase::SetOutputOrigin() .
 Else the output information is copied from the input speed image.

Implementation of this class is based on Chapter 8 of \"Level Set
Methods and Fast Marching Methods\", J.A. Sethian, Cambridge Press,
Second edition, 1999.

For an alternative implementation, see itk::FastMarchingImageFilter .

TTraits

traits


See:
 FastMarchingImageFilter

 ImageFastMarchingTraits

 ImageFastMarchingTraits2

 itk::simple::FastMarchingBase for the procedural interface

 itk::FastMarchingImageFilterBase for the Doxygen on the original ITK class.


C++ includes: sitkFastMarchingBaseImageFilter.h
";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::AddTrialPoint "

Add TrialPoints point.

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::ClearTrialPoints "

Remove all TrialPoints points.

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::FastMarchingBaseImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::GetNormalizationFactor "

Set/Get the Normalization Factor for the Speed Image . The values in the Speed Image is divided by this factor. This allows the use of images with integer
pixel types to represent the speed.

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::GetStoppingValue "

Get the Fast Marching algorithm Stopping Value.

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::GetTopologyCheck "
";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::GetTrialPoints "

Get the container of Trial Points representing the initial front.

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::SetNormalizationFactor "

Set/Get the Normalization Factor for the Speed Image . The values in the Speed Image is divided by this factor. This allows the use of images with integer
pixel types to represent the speed.

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::SetStoppingValue "

Set the Fast Marching algorithm Stopping Value. The Fast Marching
algorithm is terminated when the value of the smallest trial point is
greater than the stopping value.

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::SetTopologyCheck "
";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::SetTrialPoints "

Set the container of Trial Points representing the initial front.
Trial points are represented as a VectorContainer of LevelSetNodes.

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FastMarchingBaseImageFilter::~FastMarchingBaseImageFilter "

Destructor

";


%feature("docstring") itk::simple::FastMarchingImageFilter "

Solve an Eikonal equation using Fast Marching.


Fast marching solves an Eikonal equation where the speed is always
non-negative and depends on the position only. Starting from an
initial position on the front, fast marching systematically moves the
front forward one grid point at a time.

Updates are performed using an entropy satisfy scheme where only
\"upwind\" neighborhoods are used. This implementation of Fast
Marching uses a std::priority_queue to locate the next proper grid
position to update.

Fast Marching sweeps through N grid points in (N log N) steps to
obtain the arrival time value as the front propagates through the
grid.

Implementation of this class is based on Chapter 8 of \"Level Set
Methods and Fast Marching Methods\", J.A. Sethian, Cambridge Press,
Second edition, 1999.

This class is templated over the level set image type and the speed
image type. The initial front is specified by two containers: one
containing the known points and one containing the trial points. Alive
points are those that are already part of the object, and trial points
are considered for inclusion. In order for the filter to evolve, at
least some trial points must be specified. These can for instance be
specified as the layer of pixels around the alive points.

The speed function can be specified as a speed image or a speed
constant. The speed image is set using the method SetInput() . If the
speed image is nullptr, a constant speed function is used and is
specified using method the SetSpeedConstant() .

If the speed function is constant and of value one, fast marching
results in an approximate distance function from the initial alive
points. FastMarchingImageFilter is used in the ReinitializeLevelSetImageFilter object to create a signed distance function from the zero level set.

The algorithm can be terminated early by setting an appropriate
stopping value. The algorithm terminates when the current arrival time
being processed is greater than the stopping value.

There are two ways to specify the output image information (
LargestPossibleRegion, Spacing, Origin): (a) it is copied directly
from the input speed image or (b) it is specified by the user. Default
values are used if the user does not specify all the information.

The output information is computed as follows. If the speed image is
nullptr or if the OverrideOutputInformation is set to true, the output
information is set from user specified parameters. These parameters
can be specified using methods SetOutputRegion() , SetOutputSpacing()
, SetOutputDirection() , and SetOutputOrigin() . Else if the speed
image is not nullptr, the output information is copied from the input
speed image.

For an alternative implementation, see itk::FastMarchingImageFilter .

Possible Improvements: In the current implementation,
std::priority_queue only allows taking nodes out from the front and
putting nodes in from the back. To update a value already on the heap,
a new node is added to the heap. The defunct old node is left on the
heap. When it is removed from the top, it will be recognized as
invalid and not used. Future implementations can implement the heap in
a different way allowing the values to be updated. This will generally
require some sift-up and sift-down functions and an image of back-
pointers going from the image to heap in order to locate the node
which is to be updated.


See:
 FastMarchingImageFilterBase

 LevelSetTypeDefault

 itk::simple::FastMarching for the procedural interface

 itk::FastMarchingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkFastMarchingImageFilter.h
";

%feature("docstring")  itk::simple::FastMarchingImageFilter::AddTrialPoint "

Add TrialPoints point.

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::ClearTrialPoints "

Remove all TrialPoints points.

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::FastMarchingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::GetNormalizationFactor "

Set/Get the Normalization Factor for the Speed Image . The values in the Speed Image is divided by this factor. This allows the use of images with integer
pixel types to represent the speed.

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::GetStoppingValue "

Get the Fast Marching algorithm Stopping Value.

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::GetTrialPoints "

Get the container of Trial Points representing the initial front.

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::SetNormalizationFactor "

Set/Get the Normalization Factor for the Speed Image . The values in the Speed Image is divided by this factor. This allows the use of images with integer
pixel types to represent the speed.

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::SetStoppingValue "

Set the Fast Marching algorithm Stopping Value. The Fast Marching
algorithm is terminated when the value of the smallest trial point is
greater than the stopping value.

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::SetTrialPoints "

Set the container of Trial Points representing the initial front.
Trial points are represented as a VectorContainer of LevelSetNodes.

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FastMarchingImageFilter::~FastMarchingImageFilter "

Destructor

";


%feature("docstring") itk::simple::FastMarchingUpwindGradientImageFilter "

Generates the upwind gradient field of fast marching arrival times.


This filter adds some extra functionality to its base class. While the
solution T(x) of the Eikonal equation is being generated by the base
class with the fast marching method, the filter generates the upwind
gradient vectors of T(x), storing them in an image.

Since the Eikonal equation generates the arrival times of a wave
traveling at a given speed, the generated gradient vectors can be
interpreted as the slowness (1/velocity) vectors of the front (the
quantity inside the modulus operator in the Eikonal equation).

Gradient vectors are computed using upwind finite differences, that
is, information only propagates from points where the wavefront has
already passed. This is consistent with how the fast marching method
works.

One more extra feature is the possibility to define a set of Target
points where the propagation stops. This can be used to avoid
computing the Eikonal solution for the whole domain. The front can be
stopped either when one Target point is reached or all Target points
are reached. The propagation can stop after a time TargetOffset has
passed since the stop condition is met. This way the solution is
computed a bit downstream the Target points, so that the level sets of
T(x) corresponding to the Target are smooth.

For an alternative implementation, see itk::FastMarchingUpwindGradientImageFilterBase .


Luca Antiga Ph.D. Biomedical Technologies Laboratory, Bioengineering
Department, Mario Negri Institute, Italy.

See:
 itk::simple::FastMarchingUpwindGradient for the procedural interface

 itk::FastMarchingUpwindGradientImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkFastMarchingUpwindGradientImageFilter.h
";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::AddTargetPoint "

Add TargetPoints point.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::AddTrialPoint "

Add TrialPoints point.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::ClearTargetPoints "

Remove all TargetPoints points.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::ClearTrialPoints "

Remove all TrialPoints points.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::FastMarchingUpwindGradientImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::GetGradientImage "

Get the gradient image.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::GetNormalizationFactor "

Set/Get the Normalization Factor for the Speed Image . The values in the Speed Image is divided by this factor. This allows the use of images with integer
pixel types to represent the speed.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::GetNumberOfTargets "

Get the number of targets.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::GetTargetOffset "

Get the TargetOffset ivar.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::GetTargetPoints "

Get the container of Target Points.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::GetTargetValue "

Get the arrival time corresponding to the last reached target. If
TargetReachedMode is set to NoTargets, TargetValue contains the last
(aka largest) Eikonal solution value generated.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::GetTrialPoints "
";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::SetNormalizationFactor "

Set/Get the Normalization Factor for the Speed Image . The values in the Speed Image is divided by this factor. This allows the use of images with integer
pixel types to represent the speed.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::SetNumberOfTargets "
";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::SetTargetOffset "

Set how long (in terms of arrival times) after targets are reached the
front must stop. This is useful to ensure that the level set of target
arrival time is smooth.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::SetTargetPoints "

Set the container of Target Points. If a target point is reached, the
propagation stops. Trial points are represented as a VectorContainer of LevelSetNodes.

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::SetTrialPoints "
";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FastMarchingUpwindGradientImageFilter::~FastMarchingUpwindGradientImageFilter "

Destructor

";


%feature("docstring") itk::simple::FastSymmetricForcesDemonsRegistrationFilter "

Deformably register two images using a symmetric forces demons
algorithm.


This class was contributed by Tom Vercauteren, INRIA & Mauna Kea
Technologies based on a variation of the DemonsRegistrationFilter .

FastSymmetricForcesDemonsRegistrationFilter implements the demons deformable algorithm that register two images
by computing the deformation field which will map a moving image onto
a fixed image.

A deformation field is represented as a image whose pixel type is some
vector type with at least N elements, where N is the dimension of the
fixed image. The vector type must support element access via operator
[]. It is assumed that the vector elements behave like floating point
scalars.

This class is templated over the fixed image type, moving image type
and the deformation field type.

The input fixed and moving images are set via methods SetFixedImage
and SetMovingImage respectively. An initial deformation field maybe
set via SetInitialDisplacementField or SetInput. If no initial field
is set, a zero field is used as the initial condition.

The output deformation field can be obtained via methods GetOutput or
GetDisplacementField.

This class make use of the finite difference solver hierarchy. Update
for each iteration is computed in DemonsRegistrationFunction .


Tom Vercauteren, INRIA & Mauna Kea Technologies
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/510


WARNING:
This filter assumes that the fixed image type, moving image type and
deformation field type all have the same number of dimensions.

See:
 DemonsRegistrationFilter

 DemonsRegistrationFunction

 itk::FastSymmetricForcesDemonsRegistrationFilter for the Doxygen on the original ITK class.


C++ includes: sitkFastSymmetricForcesDemonsRegistrationFilter.h
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::Execute "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::FastSymmetricForcesDemonsRegistrationFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetElapsedIterations "

Number of iterations run.


This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetIntensityDifferenceThreshold "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetMaximumUpdateStepLength "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetMetric "

Get the metric value. The metric value is the mean square difference
in intensity between the fixed image and transforming moving image
computed over the the overlapping region between the two images. This
value is calculated for the current iteration

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetRMSChange "

Set/Get the root mean squared change of the previous iteration. May
not be used by all solvers.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetUseGradientType "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::GetUseImageSpacing "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetIntensityDifferenceThreshold "

Set/Get the threshold below which the absolute difference of intensity
yields a match. When the intensities match between a moving and fixed
image pixel, the update vector (for that iteration) will be the zero
vector. Default is 0.001.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetMaximumUpdateStepLength "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetStandardDeviations "

Set the values of the StandardDeviations vector all to value

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the values of the UpdateFieldStandardDeviations vector all to
value

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetUseGradientType "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SetUseImageSpacing "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SmoothDisplacementFieldOff "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SmoothDisplacementFieldOn "

Set the value of SmoothDisplacementField to true or false
respectfully.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SmoothUpdateFieldOff "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::SmoothUpdateFieldOn "

Set the value of SmoothUpdateField to true or false respectfully.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::FastSymmetricForcesDemonsRegistrationFilter::~FastSymmetricForcesDemonsRegistrationFilter "

Destructor

";


%feature("docstring") itk::simple::FlipImageFilter "

Flips an image across user specified axes.


FlipImageFilter flips an image across user specified axes. The flip axes are set via
method SetFlipAxes( array ) where the input is a
FixedArray<bool,ImageDimension>. The image is flipped across axes for
which array[i] is true.

In terms of grid coordinates the image is flipped within the
LargestPossibleRegion of the input image. As such, the
LargestPossibleRegion of the output image is the same as the input.

In terms of geometric coordinates, the output origin is such that the
image is flipped with respect to the coordinate axes.
See:
 itk::simple::Flip for the procedural interface

 itk::FlipImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkFlipImageFilter.h
";

%feature("docstring")  itk::simple::FlipImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::FlipImageFilter::FlipAboutOriginOff "
";

%feature("docstring")  itk::simple::FlipImageFilter::FlipAboutOriginOn "

Set the value of FlipAboutOrigin to true or false respectfully.

";

%feature("docstring")  itk::simple::FlipImageFilter::FlipImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::FlipImageFilter::GetFlipAboutOrigin "

Controls how the output origin is computed. If FlipAboutOrigin is
\"On\", the flip will occur about the origin of the axis, otherwise,
the flip will occur about the center of the axis. Default is \"On\".

";

%feature("docstring")  itk::simple::FlipImageFilter::GetFlipAxes "

Set/Get the axis to be flipped. The image is flipped along axes for
which array[i] is true. Default is false.

";

%feature("docstring")  itk::simple::FlipImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::FlipImageFilter::SetFlipAboutOrigin "

Controls how the output origin is computed. If FlipAboutOrigin is
\"On\", the flip will occur about the origin of the axis, otherwise,
the flip will occur about the center of the axis. Default is \"On\".

";

%feature("docstring")  itk::simple::FlipImageFilter::SetFlipAxes "

Set/Get the axis to be flipped. The image is flipped along axes for
which array[i] is true. Default is false.

";

%feature("docstring")  itk::simple::FlipImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::FlipImageFilter::~FlipImageFilter "

Destructor

";


%feature("docstring") itk::simple::ForwardFFTImageFilter "

Base class for forward Fast Fourier Transform .


This is a base class for the \"forward\" or \"direct\" discrete
Fourier Transform . This is an abstract base class: the actual implementation is
provided by the best child class available on the system when the
object is created via the object factory system.

This class transforms a real input image into its full complex Fourier
transform. The Fourier transform of a real input image has Hermitian
symmetry: $ f(\\\\mathbf{x}) = f^*(-\\\\mathbf{x}) $ . That is, when the result of the transform is split in half along
the x-dimension, the values in the second half of the transform are
the complex conjugates of values in the first half reflected about the
center of the image in each dimension.

This filter works only for real single-component input image types.

The output generated from a ForwardFFTImageFilter is in the dual space or frequency domain. Refer to FrequencyFFTLayoutImageRegionConstIteratorWithIndex for a description of the layout of frequencies generated after a
forward FFT. Also see ITKImageFrequency for a set of filters requiring
input images in the frequency domain.


See:
 InverseFFTImageFilter , FFTComplexToComplexImageFilter

 itk::simple::ForwardFFT for the procedural interface

 itk::ForwardFFTImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkForwardFFTImageFilter.h
";

%feature("docstring")  itk::simple::ForwardFFTImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ForwardFFTImageFilter::ForwardFFTImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ForwardFFTImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ForwardFFTImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ForwardFFTImageFilter::~ForwardFFTImageFilter "

Destructor

";


%feature("docstring") itk::simple::FunctionCommand "

A Command class which allows setting an external function, or member function.

C++ includes: sitkFunctionCommand.h
";

%feature("docstring")  itk::simple::FunctionCommand::Execute "

The method that defines action to be taken by the command

";

%feature("docstring")  itk::simple::FunctionCommand::FunctionCommand "
";

%feature("docstring")  itk::simple::FunctionCommand::SetCallbackFunction "

Generic method to set a class's member function to be called in the
Execute method.

";

%feature("docstring")  itk::simple::FunctionCommand::SetCallbackFunction "

Set a C-Style function to be called in the Execute method

";

%feature("docstring")  itk::simple::FunctionCommand::SetCallbackFunction "

Set a C-Style function with a void* clientData as an argument. The
caller is responsible for managing the life of the clientData and that
it's valid when Execute is called with the clientData.

";

%feature("docstring")  itk::simple::FunctionCommand::SetCallbackFunction "

Set as a C++ function, which is compatible with lambdas.

";

%feature("docstring")  itk::simple::FunctionCommand::~FunctionCommand "
";


%feature("docstring") itk::simple::GaborImageSource "

Generate an n-dimensional image of a Gabor filter.


GaborImageSource generates an image of either the real (i.e. symmetric) or complex
(i.e. antisymmetric) part of the Gabor filter with the orientation
directed along the x-axis. The GaborKernelFunction is used to evaluate the contribution along the x-axis whereas a non-
normalized 1-D Gaussian envelope provides the contribution in each of
the remaining N dimensions. Orientation can be manipulated via the Transform classes of the toolkit.

The output image may be of any dimension.

This implementation was contributed as a paper to the Insight Journal https://hdl.handle.net/1926/500
See:
 itk::simple::GaborSource for the procedural interface

 itk::GaborImageSource for the Doxygen on the original ITK class.


C++ includes: sitkGaborImageSource.h
";

%feature("docstring")  itk::simple::GaborImageSource::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GaborImageSource::GaborImageSource "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GaborImageSource::GetDirection "
";

%feature("docstring")  itk::simple::GaborImageSource::GetFrequency "

Set/Get the modulation frequency of the sine or cosine component.

";

%feature("docstring")  itk::simple::GaborImageSource::GetMean "

Set/Get the mean in each direction.

";

%feature("docstring")  itk::simple::GaborImageSource::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GaborImageSource::GetOrigin "
";

%feature("docstring")  itk::simple::GaborImageSource::GetOutputPixelType "
";

%feature("docstring")  itk::simple::GaborImageSource::GetSigma "

Set/Get the the standard deviation in each direction.

";

%feature("docstring")  itk::simple::GaborImageSource::GetSize "
";

%feature("docstring")  itk::simple::GaborImageSource::GetSpacing "
";

%feature("docstring")  itk::simple::GaborImageSource::SetDirection "
";

%feature("docstring")  itk::simple::GaborImageSource::SetFrequency "

Set/Get the modulation frequency of the sine or cosine component.

";

%feature("docstring")  itk::simple::GaborImageSource::SetMean "

Set/Get the mean in each direction.

";

%feature("docstring")  itk::simple::GaborImageSource::SetMean "

Set the values of the Mean vector all to value

";

%feature("docstring")  itk::simple::GaborImageSource::SetOrigin "
";

%feature("docstring")  itk::simple::GaborImageSource::SetOutputPixelType "
";

%feature("docstring")  itk::simple::GaborImageSource::SetSigma "

Set/Get the the standard deviation in each direction.

";

%feature("docstring")  itk::simple::GaborImageSource::SetSigma "

Set the values of the Sigma vector all to value

";

%feature("docstring")  itk::simple::GaborImageSource::SetSize "
";

%feature("docstring")  itk::simple::GaborImageSource::SetSpacing "
";

%feature("docstring")  itk::simple::GaborImageSource::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GaborImageSource::~GaborImageSource "

Destructor

";


%feature("docstring") itk::simple::GaussianImageSource "

Generate an n-dimensional image of a Gaussian.


GaussianImageSource generates an image of a Gaussian. m_Normalized determines whether or
not the Gaussian is normalized (whether or not the sum over infinite
space is 1.0) When creating an image, it is preferable to not
normalize the Gaussian m_Scale scales the output of the Gaussian to
span a range larger than 0->1, and is typically set to the maximum
value of the output data type (for instance, 255 for uchars)

The output image may be of any dimension.
See:
 itk::simple::GaussianSource for the procedural interface

 itk::GaussianImageSource for the Doxygen on the original ITK class.


C++ includes: sitkGaussianImageSource.h
";

%feature("docstring")  itk::simple::GaussianImageSource::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GaussianImageSource::GaussianImageSource "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GaussianImageSource::GetDirection "
";

%feature("docstring")  itk::simple::GaussianImageSource::GetMean "

Set/Get the mean in each direction.

";

%feature("docstring")  itk::simple::GaussianImageSource::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GaussianImageSource::GetNormalized "

Set/Get whether or not to normalize the Gaussian. Default is false.

";

%feature("docstring")  itk::simple::GaussianImageSource::GetOrigin "
";

%feature("docstring")  itk::simple::GaussianImageSource::GetOutputPixelType "
";

%feature("docstring")  itk::simple::GaussianImageSource::GetScale "

Gets and sets for Gaussian parameters Set/Get the scale factor to
multiply the true value of the Gaussian.

";

%feature("docstring")  itk::simple::GaussianImageSource::GetSigma "

Set/Get the standard deviation in each direction.

";

%feature("docstring")  itk::simple::GaussianImageSource::GetSize "
";

%feature("docstring")  itk::simple::GaussianImageSource::GetSpacing "
";

%feature("docstring")  itk::simple::GaussianImageSource::NormalizedOff "
";

%feature("docstring")  itk::simple::GaussianImageSource::NormalizedOn "

Set the value of Normalized to true or false respectfully.

";

%feature("docstring")  itk::simple::GaussianImageSource::SetDirection "
";

%feature("docstring")  itk::simple::GaussianImageSource::SetMean "

Set/Get the mean in each direction.

";

%feature("docstring")  itk::simple::GaussianImageSource::SetMean "

Set the values of the Mean vector all to value

";

%feature("docstring")  itk::simple::GaussianImageSource::SetNormalized "

Set/Get whether or not to normalize the Gaussian. Default is false.

";

%feature("docstring")  itk::simple::GaussianImageSource::SetOrigin "
";

%feature("docstring")  itk::simple::GaussianImageSource::SetOutputPixelType "
";

%feature("docstring")  itk::simple::GaussianImageSource::SetScale "

Gets and sets for Gaussian parameters Set/Get the scale factor to
multiply the true value of the Gaussian.

";

%feature("docstring")  itk::simple::GaussianImageSource::SetSigma "

Set/Get the standard deviation in each direction.

";

%feature("docstring")  itk::simple::GaussianImageSource::SetSigma "

Set the values of the Sigma vector all to value

";

%feature("docstring")  itk::simple::GaussianImageSource::SetSize "
";

%feature("docstring")  itk::simple::GaussianImageSource::SetSpacing "
";

%feature("docstring")  itk::simple::GaussianImageSource::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GaussianImageSource::~GaussianImageSource "

Destructor

";


%feature("docstring") itk::simple::GenericException "

The base SimpleITK exception class.

C++ includes: sitkExceptionObject.h
";

%feature("docstring")  itk::simple::GenericException::GenericException "

Default constructor. Needed to ensure the exception object can be
copied.

";

%feature("docstring")  itk::simple::GenericException::GenericException "
";

%feature("docstring")  itk::simple::GenericException::GenericException "

Constructor. Needed to ensure the exception object can be copied.

";

%feature("docstring")  itk::simple::GenericException::GenericException "

Constructor. Needed to ensure the exception object can be copied.

";

%feature("docstring")  itk::simple::GenericException::GenericException "

Constructor. Needed to ensure the exception object can be copied.

";

%feature("docstring")  itk::simple::GenericException::GetDescription "
";

%feature("docstring")  itk::simple::GenericException::GetFile "

What file did the exception occur in?

";

%feature("docstring")  itk::simple::GenericException::GetLine "

What line did the exception occur in?

";

%feature("docstring")  itk::simple::GenericException::GetLocation "
";

%feature("docstring")  itk::simple::GenericException::GetNameOfClass "
";

%feature("docstring")  itk::simple::GenericException::ToString "

Return a description of the error

";

%feature("docstring")  itk::simple::GenericException::what "
";

%feature("docstring")  itk::simple::GenericException::~GenericException "

Virtual destructor needed for subclasses. Has to have empty noexcept.

";


%feature("docstring") itk::simple::GeodesicActiveContourLevelSetImageFilter "

Segments structures in images based on a user supplied edge potential
map.


IMPORTANT
The SegmentationLevelSetImageFilter class and the GeodesicActiveContourLevelSetFunction class contain additional information necessary to gain full
understanding of how to use this filter.
OVERVIEW
This class is a level set method segmentation filter. An initial
contour is propagated outwards (or inwards) until it ''sticks'' to the
shape boundaries. This is done by using a level set speed function
based on a user supplied edge potential map.
INPUTS
This filter requires two inputs. The first input is a initial level
set. The initial level set is a real image which contains the initial
contour/surface as the zero level set. For example, a signed distance
function from the initial contour/surface is typically used. Unlike
the simpler ShapeDetectionLevelSetImageFilter the initial contour does not have to lie wholly within the shape to
be segmented. The initial contour is allow to overlap the shape
boundary. The extra advection term in the update equation behaves like
a doublet and attracts the contour to the boundary. This approach for
segmentation follows that of Caselles et al (1997).

The second input is the feature image. For this filter, this is the
edge potential map. General characteristics of an edge potential map
is that it has values close to zero in regions near the edges and
values close to one inside the shape itself. Typically, the edge
potential map is compute from the image gradient, for example:
\\\\[ g(I) = 1 / ( 1 + | (\\\\nabla * G)(I)| ) \\\\] \\\\[ g(I) = \\\\exp^{-|(\\\\nabla * G)(I)|} \\\\]

where $ I $ is image intensity and $ (\\\\nabla * G) $ is the derivative of Gaussian operator.


See SegmentationLevelSetImageFilter and SparseFieldLevelSetImageFilter for more information on Inputs.
PARAMETERS
The PropagationScaling parameter can be used to switch from
propagation outwards (POSITIVE scaling parameter) versus propagating
inwards (NEGATIVE scaling parameter).
 This implementation allows the user to set the weights between the
propagation, advection and curvature term using methods SetPropagationScaling() , SetAdvectionScaling() , SetCurvatureScaling() . In general, the larger the CurvatureScaling, the smoother the
resulting contour. To follow the implementation in Caselles et al
paper, set the PropagationScaling to $ c $ (the inflation or ballon force) and AdvectionScaling and
CurvatureScaling both to 1.0.

OUTPUTS
The filter outputs a single, scalar, real-valued image. Negative
values in the output image represent the inside of the segmented
region and positive values in the image represent the outside of the
segmented region. The zero crossings of the image correspond to the
position of the propagating front.

See SparseFieldLevelSetImageFilter and SegmentationLevelSetImageFilter for more information.
REFERENCES

\"Geodesic Active Contours\", V. Caselles, R. Kimmel and G. Sapiro.
International Journal on Computer Vision, Vol 22, No. 1, pp 61-97,
1997

See:
 SegmentationLevelSetImageFilter

 GeodesicActiveContourLevelSetFunction

 SparseFieldLevelSetImageFilter

 itk::simple::GeodesicActiveContourLevelSet for the procedural interface

 itk::GeodesicActiveContourLevelSetImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGeodesicActiveContourLevelSetImageFilter.h
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::Execute "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GeodesicActiveContourLevelSetImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GetAdvectionScaling "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GetCurvatureScaling "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GetElapsedIterations "

Number of iterations run.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GetPropagationScaling "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::GetRMSChange "

The Root Mean Square of the levelset upon termination.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::ReverseExpansionDirectionOff "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::ReverseExpansionDirectionOn "

Set the value of ReverseExpansionDirection to true or false
respectfully.

";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::SetAdvectionScaling "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::SetCurvatureScaling "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::SetPropagationScaling "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::SetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GeodesicActiveContourLevelSetImageFilter::~GeodesicActiveContourLevelSetImageFilter "

Destructor

";


%feature("docstring") itk::simple::GradientAnisotropicDiffusionImageFilter "

This filter performs anisotropic diffusion on a scalar itk::Image using the classic Perona-Malik, gradient magnitude based equation
implemented in itkGradientNDAnisotropicDiffusionFunction. For detailed
information on anisotropic diffusion, see
itkAnisotropicDiffusionFunction and
itkGradientNDAnisotropicDiffusionFunction.

Inputs and Outputs
The input to this filter should be a scalar itk::Image of any dimensionality. The output image will be a diffused copy of
the input.
Parameters
Please see the description of parameters given in
itkAnisotropicDiffusionImageFilter.

See:
 AnisotropicDiffusionImageFilter

 AnisotropicDiffusionFunction

 GradientAnisotropicDiffusionFunction

 itk::simple::GradientAnisotropicDiffusion for the procedural interface

 itk::GradientAnisotropicDiffusionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGradientAnisotropicDiffusionImageFilter.h
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::EstimateOptimalTimeStep "

This method autmatically sets the optimal timestep for an image given
its spacing.

";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::Execute "
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::GetConductanceParameter "
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::GetConductanceScalingUpdateInterval "
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::GetTimeStep "
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::GradientAnisotropicDiffusionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::SetConductanceParameter "
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::SetConductanceScalingUpdateInterval "
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::SetTimeStep "
";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GradientAnisotropicDiffusionImageFilter::~GradientAnisotropicDiffusionImageFilter "

Destructor

";


%feature("docstring") itk::simple::GradientImageFilter "

Computes the gradient of an image using directional derivatives.


Computes the gradient of an image using directional derivatives. The
directional derivative at each pixel location is computed by
convolution with a first-order derivative operator.

The second template parameter defines the value type used in the
derivative operator (defaults to float). The third template parameter
defines the value type used for output image (defaults to float). The
output image is defined as a covariant vector image whose value type
is specified as this third template parameter.


See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::Gradient for the procedural interface

 itk::GradientImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGradientImageFilter.h
";

%feature("docstring")  itk::simple::GradientImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GradientImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GradientImageFilter::GetUseImageDirection "

The UseImageDirection flag determines whether image derivatives are
computed with respect to the image grid or with respect to the
physical space. When this flag is ON the derivatives are computed with
respect to the coordinate system of physical space. The difference is
whether we take into account the image Direction or not. The flag ON
will take into account the image direction and will result in an extra
matrix multiplication compared to the amount of computation performed
when the flag is OFF. The default value of this flag is On.

";

%feature("docstring")  itk::simple::GradientImageFilter::GetUseImageSpacing "
";

%feature("docstring")  itk::simple::GradientImageFilter::GradientImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GradientImageFilter::SetUseImageDirection "

The UseImageDirection flag determines whether image derivatives are
computed with respect to the image grid or with respect to the
physical space. When this flag is ON the derivatives are computed with
respect to the coordinate system of physical space. The difference is
whether we take into account the image Direction or not. The flag ON
will take into account the image direction and will result in an extra
matrix multiplication compared to the amount of computation performed
when the flag is OFF. The default value of this flag is On.

";

%feature("docstring")  itk::simple::GradientImageFilter::SetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::GradientImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GradientImageFilter::UseImageDirectionOff "
";

%feature("docstring")  itk::simple::GradientImageFilter::UseImageDirectionOn "

Set the value of UseImageDirection to true or false respectfully.

";

%feature("docstring")  itk::simple::GradientImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::GradientImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::GradientImageFilter::~GradientImageFilter "

Destructor

";


%feature("docstring") itk::simple::GradientMagnitudeImageFilter "

Computes the gradient magnitude of an image region at each pixel.



See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::GradientMagnitude for the procedural interface

 itk::GradientMagnitudeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGradientMagnitudeImageFilter.h
";

%feature("docstring")  itk::simple::GradientMagnitudeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GradientMagnitudeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GradientMagnitudeImageFilter::GetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::GradientMagnitudeImageFilter::GradientMagnitudeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GradientMagnitudeImageFilter::SetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::GradientMagnitudeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GradientMagnitudeImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::GradientMagnitudeImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::GradientMagnitudeImageFilter::~GradientMagnitudeImageFilter "

Destructor

";


%feature("docstring") itk::simple::GradientMagnitudeRecursiveGaussianImageFilter "

Computes the Magnitude of the Gradient of an image by convolution with
the first derivative of a Gaussian.


This filter is implemented using the recursive gaussian filters
See:
 itk::simple::GradientMagnitudeRecursiveGaussian for the procedural interface

 itk::GradientMagnitudeRecursiveGaussianImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGradientMagnitudeRecursiveGaussianImageFilter.h
";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::Execute "
";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::GetNormalizeAcrossScale "

Define which normalization factor will be used for the Gaussian
See:
 RecursiveGaussianImageFilter::SetNormalizeAcrossScale


";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::GetSigma "

Set Sigma value. Sigma is measured in the units of image spacing.

";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::GradientMagnitudeRecursiveGaussianImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::NormalizeAcrossScaleOff "
";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::NormalizeAcrossScaleOn "

Set the value of NormalizeAcrossScale to true or false respectfully.

";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::SetNormalizeAcrossScale "

Define which normalization factor will be used for the Gaussian
See:
 RecursiveGaussianImageFilter::SetNormalizeAcrossScale


";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::SetSigma "

Set Sigma value. Sigma is measured in the units of image spacing.

";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GradientMagnitudeRecursiveGaussianImageFilter::~GradientMagnitudeRecursiveGaussianImageFilter "

Destructor

";


%feature("docstring") itk::simple::GradientRecursiveGaussianImageFilter "

Computes the gradient of an image by convolution with the first
derivative of a Gaussian.


This filter is implemented using the recursive gaussian filters.

This filter supports both scalar and vector pixel types within the
input image, including VectorImage type.
See:
 itk::simple::GradientRecursiveGaussian for the procedural interface

 itk::GradientRecursiveGaussianImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGradientRecursiveGaussianImageFilter.h
";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::GetNormalizeAcrossScale "

Define which normalization factor will be used for the Gaussian
See:
 RecursiveGaussianImageFilter::SetNormalizeAcrossScale


";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::GetSigma "
";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::GetUseImageDirection "

The UseImageDirection flag determines whether the gradients are
computed with respect to the image grid or with respect to the
physical space. When this flag is ON the gradients are computed with
respect to the coordinate system of physical space. The difference is
whether we take into account the image Direction or not. The flag ON
will take into account the image direction and will result in an extra
matrix multiplication compared to the amount of computation performed
when the flag is OFF. The default value of this flag is On.

";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::GradientRecursiveGaussianImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::NormalizeAcrossScaleOff "
";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::NormalizeAcrossScaleOn "

Set the value of NormalizeAcrossScale to true or false respectfully.

";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::SetNormalizeAcrossScale "

Define which normalization factor will be used for the Gaussian
See:
 RecursiveGaussianImageFilter::SetNormalizeAcrossScale


";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::SetSigma "

Set Sigma value. Sigma is measured in the units of image spacing.

";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::SetUseImageDirection "

The UseImageDirection flag determines whether the gradients are
computed with respect to the image grid or with respect to the
physical space. When this flag is ON the gradients are computed with
respect to the coordinate system of physical space. The difference is
whether we take into account the image Direction or not. The flag ON
will take into account the image direction and will result in an extra
matrix multiplication compared to the amount of computation performed
when the flag is OFF. The default value of this flag is On.

";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::UseImageDirectionOff "
";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::UseImageDirectionOn "

Set the value of UseImageDirection to true or false respectfully.

";

%feature("docstring")  itk::simple::GradientRecursiveGaussianImageFilter::~GradientRecursiveGaussianImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleConnectedClosingImageFilter "

Enhance pixels associated with a dark object (identified by a seed
pixel) where the dark object is surrounded by a brighter object.


GrayscaleConnectedClosingImagefilter is useful for enhancing dark
objects that are surrounded by bright borders. This filter makes it
easier to threshold the image and extract just the object of interest.

Geodesic morphology and the connected closing algorithm are described
in Chapter 6 of Pierre Soille's book \"Morphological Image Analysis:
Principles and Applications\", Second Edition, Springer, 2003.


See:
 GrayscaleGeodesicDilateImageFilter

 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::GrayscaleConnectedClosing for the procedural interface

 itk::GrayscaleConnectedClosingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleConnectedClosingImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::GetSeed "

Set/Get the seed pixel for the segmentation

";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::GrayscaleConnectedClosingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::SetSeed "

Set/Get the seed pixel for the segmentation

";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleConnectedClosingImageFilter::~GrayscaleConnectedClosingImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleConnectedOpeningImageFilter "

Enhance pixels associated with a bright object (identified by a seed
pixel) where the bright object is surrounded by a darker object.


GrayscaleConnectedOpeningImagefilter is useful for enhancing bright
objects that are surrounded by dark borders. This filter makes it
easier to threshold the image and extract just the object of interest.

Geodesic morphology and the connected opening algorithm is described
in Chapter 6 of Pierre Soille's book \"Morphological Image Analysis:
Principles and Applications\", Second Edition, Springer, 2003.


See:
 GrayscaleGeodesicDilateImageFilter

 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::GrayscaleConnectedOpening for the procedural interface

 itk::GrayscaleConnectedOpeningImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleConnectedOpeningImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::GetSeed "

Set/Get the seed pixel for the segmentation

";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::GrayscaleConnectedOpeningImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::SetSeed "

Set/Get the seed pixel for the segmentation

";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleConnectedOpeningImageFilter::~GrayscaleConnectedOpeningImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleDilateImageFilter "

Grayscale dilation of an image.


Dilate an image using grayscale morphology. Dilation takes the maximum
of all the pixels identified by the structuring element.

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.


See:
 MorphologyImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::GrayscaleDilate for the procedural interface

 itk::GrayscaleDilateImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleDilateImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::GrayscaleDilateImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleDilateImageFilter::~GrayscaleDilateImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleErodeImageFilter "

Grayscale erosion of an image.


Erode an image using grayscale morphology. Erosion takes the maximum
of all the pixels identified by the structuring element.

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.


See:
 MorphologyImageFilter , GrayscaleFunctionErodeImageFilter , BinaryErodeImageFilter

 itk::simple::GrayscaleErode for the procedural interface

 itk::GrayscaleErodeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleErodeImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::GrayscaleErodeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleErodeImageFilter::~GrayscaleErodeImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleFillholeImageFilter "

Remove local minima not connected to the boundary of the image.


GrayscaleFillholeImageFilter fills holes in a grayscale image. Holes are local minima in the
grayscale topography that are not connected to boundaries of the
image. Gray level values adjacent to a hole are extrapolated across
the hole.

This filter is used to smooth over local minima without affecting the
values of local maxima. If you take the difference between the output
of this filter and the original image (and perhaps threshold the
difference above a small value), you'll obtain a map of the local
minima.

This filter uses the ReconstructionByErosionImageFilter . It provides its own input as the \"mask\" input to the geodesic
erosion. The \"marker\" image for the geodesic erosion is constructed
such that boundary pixels match the boundary pixels of the input image
and the interior pixels are set to the maximum pixel value in the
input image.

Geodesic morphology and the Fillhole algorithm is described in Chapter
6 of Pierre Soille's book \"Morphological Image Analysis: Principles
and Applications\", Second Edition, Springer, 2003.


See:
 ReconstructionByErosionImageFilter

 MorphologyImageFilter , GrayscaleErodeImageFilter , GrayscaleFunctionErodeImageFilter , BinaryErodeImageFilter

 itk::simple::GrayscaleFillhole for the procedural interface

 itk::GrayscaleFillholeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleFillholeImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleFillholeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GrayscaleFillholeImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::GrayscaleFillholeImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleFillholeImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleFillholeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleFillholeImageFilter::GrayscaleFillholeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleFillholeImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleFillholeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleFillholeImageFilter::~GrayscaleFillholeImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleGeodesicDilateImageFilter "

geodesic gray scale dilation of an image


Geodesic dilation operates on a \"marker\" image and a \"mask\" image.
The marker image is dilated using an elementary structuring element
(neighborhood of radius one using only the face connected neighbors).
The resulting image is then compared with the mask image. The output
image is the pixelwise minimum of the dilated marker image and the
mask image.

Geodesic dilation is run either one iteration or until convergence. In
the convergence case, the filter is equivalent to \"reconstruction by
dilation\". This filter is implemented to handle both scenarios. The
one iteration case is multi-threaded. The convergence case is
delegated to another instance of the same filter (but configured to
run a single iteration).

The marker image must be less than or equal to the mask image (on a
pixel by pixel basis).

Geodesic morphology is described in Chapter 6 of Pierre Soille's book
\"Morphological Image Analysis: Principles and Applications\", Second
Edition, Springer, 2003.

A noniterative version of this algorithm can be found in the ReconstructionByDilationImageFilter . This noniterative solution is much faster than the implementation
provided here. All ITK filters that previously used
GrayscaleGeodesicDiliateImageFilter as part of their implementation
have been converted to use the ReconstructionByDilationImageFilter . The GrayscaleGeodesicDilateImageFilter is maintained for backward compatibility.


See:
 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter , ReconstructionByDilationImageFilter

 itk::simple::GrayscaleGeodesicDilate for the procedural interface

 itk::GrayscaleGeodesicDilateImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleGeodesicDilateImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::GetRunOneIteration "

Set/Get whether the filter should run one iteration or until
convergence. When run to convergence, this filter is equivalent to
\"reconstruction by dilation\". Default is off.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::GrayscaleGeodesicDilateImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::RunOneIterationOff "
";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::RunOneIterationOn "

Set the value of RunOneIteration to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::SetRunOneIteration "

Set/Get whether the filter should run one iteration or until
convergence. When run to convergence, this filter is equivalent to
\"reconstruction by dilation\". Default is off.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleGeodesicDilateImageFilter::~GrayscaleGeodesicDilateImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleGeodesicErodeImageFilter "

geodesic gray scale erosion of an image


Geodesic erosion operates on a \"marker\" image and a \"mask\" image.
The marker image is eroded using an elementary structuring element
(neighborhood of radius one using only the face connected neighbors).
The resulting image is then compared with the mask image. The output
image is the pixelwise maximum of the eroded marker image and the mask
image.

Geodesic erosion is run either one iteration or until convergence. In
the convergence case, the filter is equivalent to \"reconstruction by
erosion\". This filter is implemented to handle both scenarios. The
one iteration case is multi-threaded. The convergence case is
delegated to another instance of the same filter (but configured to
run a single iteration).

The marker image must be greater than or equal to the mask image (on a
pixel by pixel basis).

Geodesic morphology is described in Chapter 6 of Pierre Soille's book
\"Morphological Image Analysis: Principles and Applications\", Second
Edition, Springer, 2003.

A noniterative version of this algorithm can be found in the ReconstructionByErosionImageFilter . This noniterative solution is much faster than the implementation
provided here. All ITK filters that previously used GrayscaleGeodesicErodeImageFilter as part of their implementation have been converted to use the ReconstructionByErosionImageFilter . The GrayscaleGeodesicErodeImageFilter is maintained for backward compatibility.


See:
 MorphologyImageFilter , GrayscaleErodeImageFilter , GrayscaleFunctionErodeImageFilter , BinaryErodeImageFilter , ReconstructionByErosionImageFilter

 itk::simple::GrayscaleGeodesicErode for the procedural interface

 itk::GrayscaleGeodesicErodeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleGeodesicErodeImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::GetRunOneIteration "

Set/Get whether the filter should run one iteration or until
convergence. When run to convergence, this filter is equivalent to
\"reconstruction by erosion\". Default is off.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::GrayscaleGeodesicErodeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::RunOneIterationOff "
";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::RunOneIterationOn "

Set the value of RunOneIteration to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::SetRunOneIteration "

Set/Get whether the filter should run one iteration or until
convergence. When run to convergence, this filter is equivalent to
\"reconstruction by erosion\". Default is off.

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleGeodesicErodeImageFilter::~GrayscaleGeodesicErodeImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleGrindPeakImageFilter "

Remove local maxima not connected to the boundary of the image.


GrayscaleGrindPeakImageFilter removes peaks in a grayscale image. Peaks are local maxima in the
grayscale topography that are not connected to boundaries of the
image. Gray level values adjacent to a peak are extrapolated through
the peak.

This filter is used to smooth over local maxima without affecting the
values of local minima. If you take the difference between the output
of this filter and the original image (and perhaps threshold the
difference above a small value), you'll obtain a map of the local
maxima.

This filter uses the GrayscaleGeodesicDilateImageFilter . It provides its own input as the \"mask\" input to the geodesic
erosion. The \"marker\" image for the geodesic erosion is constructed
such that boundary pixels match the boundary pixels of the input image
and the interior pixels are set to the minimum pixel value in the
input image.

This filter is the dual to the GrayscaleFillholeImageFilter which implements the Fillhole algorithm. Since it is a dual, it is
somewhat superfluous but is provided as a convenience.

Geodesic morphology and the Fillhole algorithm is described in Chapter
6 of Pierre Soille's book \"Morphological Image Analysis: Principles
and Applications\", Second Edition, Springer, 2003.


See:
 GrayscaleGeodesicDilateImageFilter

 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::GrayscaleGrindPeak for the procedural interface

 itk::GrayscaleGrindPeakImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleGrindPeakImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleGrindPeakImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GrayscaleGrindPeakImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::GrayscaleGrindPeakImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleGrindPeakImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleGrindPeakImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleGrindPeakImageFilter::GrayscaleGrindPeakImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleGrindPeakImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::GrayscaleGrindPeakImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleGrindPeakImageFilter::~GrayscaleGrindPeakImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleMorphologicalClosingImageFilter "

gray scale dilation of an image


Erode an image using grayscale morphology. Dilation takes the maximum
of all the pixels identified by the structuring element.

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.


See:
 MorphologyImageFilter , GrayscaleFunctionErodeImageFilter , BinaryErodeImageFilter

 itk::simple::GrayscaleMorphologicalClosing for the procedural interface

 itk::GrayscaleMorphologicalClosingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleMorphologicalClosingImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::GetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::GrayscaleMorphologicalClosingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::SafeBorderOff "
";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::SafeBorderOn "

Set the value of SafeBorder to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::SetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalClosingImageFilter::~GrayscaleMorphologicalClosingImageFilter "

Destructor

";


%feature("docstring") itk::simple::GrayscaleMorphologicalOpeningImageFilter "

gray scale dilation of an image


Dilate an image using grayscale morphology. Dilation takes the maximum
of all the pixels identified by the structuring element.

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.


See:
 MorphologyImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::GrayscaleMorphologicalOpening for the procedural interface

 itk::GrayscaleMorphologicalOpeningImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGrayscaleMorphologicalOpeningImageFilter.h
";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::GetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::GrayscaleMorphologicalOpeningImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::SafeBorderOff "
";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::SafeBorderOn "

Set the value of SafeBorder to true or false respectfully.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::SetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GrayscaleMorphologicalOpeningImageFilter::~GrayscaleMorphologicalOpeningImageFilter "

Destructor

";


%feature("docstring") itk::simple::GreaterEqualImageFilter "

Implements pixel-wise generic operation of two images, or of an image
and a constant.


This class is parameterized over the types of the two input images and
the type of the output image. It is also parameterized by the
operation to be applied. A Functor style is used.

The constant must be of the same type than the pixel type of the
corresponding image. It is wrapped in a SimpleDataObjectDecorator so it can be updated through the pipeline. The SetConstant() and
GetConstant() methods are provided as shortcuts to set or get the
constant value without manipulating the decorator.


See:
 BinaryGeneratorImagFilter

 UnaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::GreaterEqual for the procedural interface

 itk::BinaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGreaterEqualImageFilter.h
";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::Execute "

Execute the filter on an image and a constant with the given
parameters

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::GetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::GetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::GreaterEqualImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::SetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::SetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GreaterEqualImageFilter::~GreaterEqualImageFilter "

Destructor

";


%feature("docstring") itk::simple::GreaterImageFilter "

Implements pixel-wise generic operation of two images, or of an image
and a constant.


This class is parameterized over the types of the two input images and
the type of the output image. It is also parameterized by the
operation to be applied. A Functor style is used.

The constant must be of the same type than the pixel type of the
corresponding image. It is wrapped in a SimpleDataObjectDecorator so it can be updated through the pipeline. The SetConstant() and
GetConstant() methods are provided as shortcuts to set or get the
constant value without manipulating the decorator.


See:
 BinaryGeneratorImagFilter

 UnaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::Greater for the procedural interface

 itk::BinaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkGreaterImageFilter.h
";

%feature("docstring")  itk::simple::GreaterImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::GreaterImageFilter::Execute "
";

%feature("docstring")  itk::simple::GreaterImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::GreaterImageFilter::Execute "
";

%feature("docstring")  itk::simple::GreaterImageFilter::Execute "
";

%feature("docstring")  itk::simple::GreaterImageFilter::Execute "

Execute the filter on an image and a constant with the given
parameters

";

%feature("docstring")  itk::simple::GreaterImageFilter::Execute "
";

%feature("docstring")  itk::simple::GreaterImageFilter::GetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::GreaterImageFilter::GetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::GreaterImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GreaterImageFilter::GreaterImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GreaterImageFilter::SetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::GreaterImageFilter::SetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::GreaterImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GreaterImageFilter::~GreaterImageFilter "

Destructor

";


%feature("docstring") itk::simple::GridImageSource "

Generate an n-dimensional image of a grid.


GridImageSource generates an image of a grid. From the abstract... \"Certain classes
of images find disparate use amongst members of the ITK community for
such purposes as visualization, simulation, testing, etc. Currently
there exists two derived classes from the ImageSource class used for
generating specific images for various applications, viz.
RandomImageSource and GaussianImageSource . We propose to add to this
set with the class GridImageSource which, obviously enough, produces a
grid image. Such images are useful for visualizing deformation when
used in conjunction with the WarpImageFilter , simulating magnetic
resonance tagging images, or creating optical illusions with which to
amaze your friends.\"

The output image may be of any dimension.


Tustison N., Avants B., Gee J. University of Pennsylvania
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/475
See:
 itk::simple::GridSource for the procedural interface

 itk::GridImageSource for the Doxygen on the original ITK class.


C++ includes: sitkGridImageSource.h
";

%feature("docstring")  itk::simple::GridImageSource::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::GridImageSource::GetDirection "
";

%feature("docstring")  itk::simple::GridImageSource::GetGridOffset "

Set/Get the grid offset.

";

%feature("docstring")  itk::simple::GridImageSource::GetGridSpacing "

Set/Get the grid spacing of the peaks.

";

%feature("docstring")  itk::simple::GridImageSource::GetName "

Name of this class

";

%feature("docstring")  itk::simple::GridImageSource::GetOrigin "
";

%feature("docstring")  itk::simple::GridImageSource::GetOutputPixelType "
";

%feature("docstring")  itk::simple::GridImageSource::GetScale "

Set/Get the scale factor to multiply the true value of the grid.

";

%feature("docstring")  itk::simple::GridImageSource::GetSigma "

Set/Get the standard deviation of the Gaussians or width of the box
functions.

";

%feature("docstring")  itk::simple::GridImageSource::GetSize "
";

%feature("docstring")  itk::simple::GridImageSource::GetSpacing "
";

%feature("docstring")  itk::simple::GridImageSource::GridImageSource "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::GridImageSource::SetDirection "
";

%feature("docstring")  itk::simple::GridImageSource::SetGridOffset "

Set/Get the grid offset.

";

%feature("docstring")  itk::simple::GridImageSource::SetGridSpacing "

Set/Get the grid spacing of the peaks.

";

%feature("docstring")  itk::simple::GridImageSource::SetOrigin "
";

%feature("docstring")  itk::simple::GridImageSource::SetOutputPixelType "
";

%feature("docstring")  itk::simple::GridImageSource::SetScale "

Set/Get the scale factor to multiply the true value of the grid.

";

%feature("docstring")  itk::simple::GridImageSource::SetSigma "

Set/Get the standard deviation of the Gaussians or width of the box
functions.

";

%feature("docstring")  itk::simple::GridImageSource::SetSigma "

Set the values of the Sigma vector all to value

";

%feature("docstring")  itk::simple::GridImageSource::SetSize "
";

%feature("docstring")  itk::simple::GridImageSource::SetSpacing "
";

%feature("docstring")  itk::simple::GridImageSource::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::GridImageSource::~GridImageSource "

Destructor

";


%feature("docstring") itk::simple::HConcaveImageFilter "

Identify local minima whose depth below the baseline is greater than
h.


HConcaveImageFilter extract local minima that are more than h intensity units below the
(local) background. This has the effect of extracting objects that are
darker than the background by at least h intensity units.

This filter uses the HMinimaImageFilter .

Geodesic morphology and the H-Convex algorithm is described in Chapter
6 of Pierre Soille's book \"Morphological Image Analysis: Principles
and Applications\", Second Edition, Springer, 2003.


See:
 GrayscaleGeodesicDilateImageFilter , HMaximaImageFilter ,

 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::HConcave for the procedural interface

 itk::HConcaveImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkHConcaveImageFilter.h
";

%feature("docstring")  itk::simple::HConcaveImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::HConcaveImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::HConcaveImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::HConcaveImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::HConcaveImageFilter::GetHeight "

Set/Get the height that a local maximum must be above the local
background (local contrast) in order to survive the processing. Local
maxima below this value are replaced with an estimate of the local
background.

";

%feature("docstring")  itk::simple::HConcaveImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::HConcaveImageFilter::HConcaveImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::HConcaveImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::HConcaveImageFilter::SetHeight "

Set/Get the height that a local maximum must be above the local
background (local contrast) in order to survive the processing. Local
maxima below this value are replaced with an estimate of the local
background.

";

%feature("docstring")  itk::simple::HConcaveImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::HConcaveImageFilter::~HConcaveImageFilter "

Destructor

";


%feature("docstring") itk::simple::HConvexImageFilter "

Identify local maxima whose height above the baseline is greater than
h.


HConvexImageFilter extract local maxima that are more than h intensity units above the
(local) background. This has the effect of extracting objects that are
brighter than background by at least h intensity units.

This filter uses the HMaximaImageFilter .

Geodesic morphology and the H-Convex algorithm is described in Chapter
6 of Pierre Soille's book \"Morphological Image Analysis: Principles
and Applications\", Second Edition, Springer, 2003.


See:
 GrayscaleGeodesicDilateImageFilter , HMinimaImageFilter

 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::HConvex for the procedural interface

 itk::HConvexImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkHConvexImageFilter.h
";

%feature("docstring")  itk::simple::HConvexImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::HConvexImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::HConvexImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::HConvexImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::HConvexImageFilter::GetHeight "

Set/Get the height that a local maximum must be above the local
background (local contrast) in order to survive the processing. Local
maxima below this value are replaced with an estimate of the local
background.

";

%feature("docstring")  itk::simple::HConvexImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::HConvexImageFilter::HConvexImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::HConvexImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::HConvexImageFilter::SetHeight "

Set/Get the height that a local maximum must be above the local
background (local contrast) in order to survive the processing. Local
maxima below this value are replaced with an estimate of the local
background.

";

%feature("docstring")  itk::simple::HConvexImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::HConvexImageFilter::~HConvexImageFilter "

Destructor

";


%feature("docstring") itk::simple::HMaximaImageFilter "

Suppress local maxima whose height above the baseline is less than h.


HMaximaImageFilter suppresses local maxima that are less than h intensity units above
the (local) background. This has the effect of smoothing over the
\"high\" parts of the noise in the image without smoothing over large
changes in intensity (region boundaries). See the HMinimaImageFilter to suppress the local minima whose depth is less than h intensity
units below the (local) background.

If the output of HMaximaImageFilter is subtracted from the original image, the significant \"peaks\" in
the image can be identified. This is what the HConvexImageFilter provides.

This filter uses the ReconstructionByDilationImageFilter . It provides its own input as the \"mask\" input to the geodesic
dilation. The \"marker\" image for the geodesic dilation is the input
image minus the height parameter h.

Geodesic morphology and the H-Maxima algorithm is described in Chapter
6 of Pierre Soille's book \"Morphological Image Analysis: Principles
and Applications\", Second Edition, Springer, 2003.

The height parameter is set using SetHeight.


See:
 ReconstructionByDilationImageFilter , HMinimaImageFilter , HConvexImageFilter

 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::HMaxima for the procedural interface

 itk::HMaximaImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkHMaximaImageFilter.h
";

%feature("docstring")  itk::simple::HMaximaImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::HMaximaImageFilter::GetHeight "

Set/Get the height that a local maximum must be above the local
background (local contrast) in order to survive the processing. Local
maxima below this value are replaced with an estimate of the local
background.

";

%feature("docstring")  itk::simple::HMaximaImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::HMaximaImageFilter::HMaximaImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::HMaximaImageFilter::SetHeight "

Set/Get the height that a local maximum must be above the local
background (local contrast) in order to survive the processing. Local
maxima below this value are replaced with an estimate of the local
background.

";

%feature("docstring")  itk::simple::HMaximaImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::HMaximaImageFilter::~HMaximaImageFilter "

Destructor

";


%feature("docstring") itk::simple::HMinimaImageFilter "

Suppress local minima whose depth below the baseline is less than h.


HMinimaImageFilter suppresses local minima that are less than h intensity units below
the (local) background. This has the effect of smoothing over the
\"low\" parts of the noise in the image without smoothing over large
changes in intensity (region boundaries). See the HMaximaImageFilter to suppress the local maxima whose height is less than h intensity
units above the (local) background.

If original image is subtracted from the output of HMinimaImageFilter , the significant \"valleys\" in the image can be identified. This is
what the HConcaveImageFilter provides.

This filter uses the GrayscaleGeodesicErodeImageFilter . It provides its own input as the \"mask\" input to the geodesic
dilation. The \"marker\" image for the geodesic dilation is the input
image plus the height parameter h.

Geodesic morphology and the H-Minima algorithm is described in Chapter
6 of Pierre Soille's book \"Morphological Image Analysis: Principles
and Applications\", Second Edition, Springer, 2003.


See:
 GrayscaleGeodesicDilateImageFilter , HMinimaImageFilter , HConvexImageFilter

 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::HMinima for the procedural interface

 itk::HMinimaImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkHMinimaImageFilter.h
";

%feature("docstring")  itk::simple::HMinimaImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::HMinimaImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::HMinimaImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::HMinimaImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::HMinimaImageFilter::GetHeight "

Set/Get the height that a local maximum must be above the local
background (local contrast) in order to survive the processing. Local
maxima below this value are replaced with an estimate of the local
background.

";

%feature("docstring")  itk::simple::HMinimaImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::HMinimaImageFilter::HMinimaImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::HMinimaImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::HMinimaImageFilter::SetHeight "

Set/Get the height that a local maximum must be above the local
background (local contrast) in order to survive the processing. Local
maxima below this value are replaced with an estimate of the local
background.

";

%feature("docstring")  itk::simple::HMinimaImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::HMinimaImageFilter::~HMinimaImageFilter "

Destructor

";


%feature("docstring") itk::simple::HalfHermitianToRealInverseFFTImageFilter "

Base class for specialized complex-to-real inverse Fast Fourier Transform .


This is a base class for the \"inverse\" or \"reverse\" Discrete
Fourier Transform . This is an abstract base class: the actual implementation is
provided by the best child class available on the system when the
object is created via the object factory system.

The input to this filter is assumed to have the same format as the
output of the RealToHalfHermitianForwardFFTImageFilter . That is, the input is assumed to consist of roughly half the full
complex image resulting from a real-to-complex discrete Fourier
transform. This half is expected to be the first half of the image in
the X-dimension. Because this filter assumes that the input stores
only about half of the non-redundant complex pixels, the output is
larger in the X-dimension than it is in the input. To determine the
actual size of the output image, this filter needs additional
information in the form of a flag indicating whether the output image
has an odd size in the X-dimension. Use SetActualXDimensionIsOdd() to set this flag.


See:
 ForwardFFTImageFilter , HalfHermitianToRealInverseFFTImageFilter

 itk::simple::HalfHermitianToRealInverseFFT for the procedural interface

 itk::HalfHermitianToRealInverseFFTImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkHalfHermitianToRealInverseFFTImageFilter.h
";

%feature("docstring")  itk::simple::HalfHermitianToRealInverseFFTImageFilter::ActualXDimensionIsOddOff "
";

%feature("docstring")  itk::simple::HalfHermitianToRealInverseFFTImageFilter::ActualXDimensionIsOddOn "

Set the value of ActualXDimensionIsOdd to true or false respectfully.

";

%feature("docstring")  itk::simple::HalfHermitianToRealInverseFFTImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::HalfHermitianToRealInverseFFTImageFilter::GetActualXDimensionIsOdd "

Was the original truncated dimension size odd?

";

%feature("docstring")  itk::simple::HalfHermitianToRealInverseFFTImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::HalfHermitianToRealInverseFFTImageFilter::HalfHermitianToRealInverseFFTImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::HalfHermitianToRealInverseFFTImageFilter::SetActualXDimensionIsOdd "

Was the original truncated dimension size odd?

";

%feature("docstring")  itk::simple::HalfHermitianToRealInverseFFTImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::HalfHermitianToRealInverseFFTImageFilter::~HalfHermitianToRealInverseFFTImageFilter "

Destructor

";


%feature("docstring") itk::simple::HashImageFilter "

Compute the sha1 or md5 hash of an image.



See:
 itk::simple::Hash for the procedural interface


C++ includes: sitkHashImageFilter.h
";

%feature("docstring")  itk::simple::HashImageFilter::Execute "
";

%feature("docstring")  itk::simple::HashImageFilter::GetHashFunction "
";

%feature("docstring")  itk::simple::HashImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::HashImageFilter::HashImageFilter "
";

%feature("docstring")  itk::simple::HashImageFilter::SetHashFunction "
";

%feature("docstring")  itk::simple::HashImageFilter::ToString "
";

%feature("docstring")  itk::simple::HashImageFilter::~HashImageFilter "
";


%feature("docstring") itk::simple::HausdorffDistanceImageFilter "

Computes the Hausdorff distance between the set of non-zero pixels of
two images.


HausdorffDistanceImageFilter computes the distance between the set non-zero pixels of two images
using the following formula: \\\\[ H(A,B) = \\\\max(h(A,B),h(B,A)) \\\\] where \\\\[ h(A,B) = \\\\max_{a \\\\in A} \\\\min_{b \\\\in B} \\\\| a -
b\\\\| \\\\] is the directed Hausdorff distance and $A$ and $B$ are respectively the set of non-zero pixels in the first and second
input images.

In particular, this filter uses the DirectedHausdorffImageFilter
inside to compute the two directed distances and then select the
largest of the two.

The Hausdorff distance measures the degree of mismatch between two
sets and behaves like a metric over the set of all closed bounded sets
- with properties of identity, symmetry and triangle inequality.

This filter requires the largest possible region of the first image
and the same corresponding region in the second image. It behaves as
filter with two inputs and one output. Thus it can be inserted in a
pipeline with other filters. The filter passes the first input through
unmodified.

This filter is templated over the two input image types. It assume
both images have the same number of dimensions.


See:
 DirectedHausdorffDistanceImageFilter

 itk::HausdorffDistanceImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkHausdorffDistanceImageFilter.h
";

%feature("docstring")  itk::simple::HausdorffDistanceImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::HausdorffDistanceImageFilter::GetAverageHausdorffDistance "

Return the computed Hausdorff distance.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::HausdorffDistanceImageFilter::GetHausdorffDistance "

Return the computed Hausdorff distance.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::HausdorffDistanceImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::HausdorffDistanceImageFilter::HausdorffDistanceImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::HausdorffDistanceImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::HausdorffDistanceImageFilter::~HausdorffDistanceImageFilter "

Destructor

";


%feature("docstring") itk::simple::HistogramMatchingImageFilter "

Normalize the grayscale values for a source image by matching the
shape of the source image histogram to a reference histogram.


HistogramMatchingImageFilter normalizes the grayscale values of a source image based on the
grayscale values of either a reference image or a reference histogram.
This filter uses a histogram matching technique where the histograms
of the are matched only at a specified number of quantile values.

This filter was originally designed to normalize MR images of the same
MR protocol and same body part. The algorithm works best if background
pixels are excluded from both the source and reference histograms. A
simple background exclusion method is to exclude all pixels whose
grayscale values are smaller than the mean grayscale value. ThresholdAtMeanIntensityOn() switches on this simple background exclusion method. With ThresholdAtMeanIntensityOn() , The reference histogram returned from this filter will expand the
first and last bin bounds to include the minimum and maximum intensity
values of the entire reference image, but only intensity values
greater than the mean will be used to populate the histogram.

The source image can be set via either SetInput() or SetSourceImage()
. The reference object used is selected with can be set via
SetReferenceImage() or SetReferenceHistogram() .

SetNumberOfHistogramLevels() sets the number of bins used when creating histograms of the source
and reference images. SetNumberOfMatchPoints() governs the number of quantile values to be matched.

This filter assumes that both the source and reference are of the same
type and that the input and output image type have the same number of
dimension and have scalar pixel types.

REFERENCE
Laszlo G. Nyul, Jayaram K. Udupa, and Xuan Zhang, \"New Variants of a
Method of MRI Scale Standardization\", IEEE Transactions on Medical
Imaging, 19(2):143-150, 2000.

See:
 itk::simple::HistogramMatching for the procedural interface

 itk::HistogramMatchingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkHistogramMatchingImageFilter.h
";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::GetNumberOfHistogramLevels "

Set/Get the number of histogram levels used.

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::GetNumberOfMatchPoints "

Set/Get the number of match points used.

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::GetThresholdAtMeanIntensity "

Set/Get the threshold at mean intensity flag. If true, only source
(reference) pixels which are greater than the mean source (reference)
intensity is used in the histogram matching. If false, all pixels are
used.

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::HistogramMatchingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::SetNumberOfHistogramLevels "

Set/Get the number of histogram levels used.

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::SetNumberOfMatchPoints "

Set/Get the number of match points used.

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::SetThresholdAtMeanIntensity "

Set/Get the threshold at mean intensity flag. If true, only source
(reference) pixels which are greater than the mean source (reference)
intensity is used in the histogram matching. If false, all pixels are
used.

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::ThresholdAtMeanIntensityOff "
";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::ThresholdAtMeanIntensityOn "

Set the value of ThresholdAtMeanIntensity to true or false
respectfully.

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::HistogramMatchingImageFilter::~HistogramMatchingImageFilter "

Destructor

";


%feature("docstring") itk::simple::HuangThresholdImageFilter "

Threshold an image using the Huang Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the HuangThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::HuangThreshold for the procedural interface

 itk::HuangThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkHuangThresholdImageFilter.h
";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::HuangThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value. The default value NumericTraits<OutputPixelType>::max()

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins. Defaults is 128.

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::HuangThresholdImageFilter::~HuangThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::Image "

The Image class for SimpleITK.


This Image class can represent 2D, 3D, and 4D images. The pixel types may be a
scalar, a multi-component vector or a run-length-encoded (RLE)
\"label\". The dimension, pixel type and size is specified at
construction.

A fundamental concept of ITK images is that they occupy physical space
where the image is defined by an origin, spacing, and direction cosine
matrix. The attributes are taken into consideration when doing most
operations on an image. A meta-data dictionary is also associated with
the image, which may contain additional fields from reading but these
attributes are not propagated by image filters.

The SimpleITK Image provides a single facade interface to several ITK image types.
Internally, the SimpleITK Image maintains a pointer to the ITK image class, and performs reference
counting and lazy copying. This means that deep copying of an image
including it's buffer is delayed until the image is modified. This
removes the need to use pointers to SimpleITK Image class, as copying and returning by value do not unnecessarily
duplicate the data.

/sa itk::Image itk::VectorImage itk::LabelMap itk::ImageBase

C++ includes: sitkImage.h
";

%feature("docstring")  itk::simple::Image::CopyInformation "

Copy common meta-data from an image to this one.


Copies the Origin, Spacing, and Direction from the source image to
this image. The meta-data dictionary is not copied.

It is required for the source Image's dimension and size to match, this image's attributes, otherwise an
exception will be generated.

";

%feature("docstring")  itk::simple::Image::EraseMetaData "

Remove an entry from the meta-data dictionary.


Returns true, when the value exists in the dictionary and is removed,
false otherwise.

";

%feature("docstring")  itk::simple::Image::GetDepth "

Get the number of pixels the Image is in the third dimension or 0 if the Image is only 2D

";

%feature("docstring")  itk::simple::Image::GetDimension "

Get the number of physical dimensions.

Only the spatial dimensions are considered here. These are the
dimensions the origin, spacing and direction cosine matrix are
applicable to. This does not include the pixels' vector index as a
dimension.

";

%feature("docstring")  itk::simple::Image::GetHeight "

Get the number of pixels the Image is in the second dimension

";

%feature("docstring")  itk::simple::Image::GetMetaData "

Get the value of a meta-data dictionary entry as a string.


If the key is not in the dictionary then an exception is thrown.

string types in the dictionary are returned as their native strings.
Other types are printed to string before returning.

";

%feature("docstring")  itk::simple::Image::GetMetaDataKeys "

get a vector of keys in from the meta-data dictionary


Returns a vector of keys to the key/value entries in the image's meta-
data dictionary. Iterate through with these keys to get the values.

";

%feature("docstring")  itk::simple::Image::GetNumberOfComponentsPerPixel "

Get the number of components for each pixel.


For scalar images this methods returns 1. For vector images the number
of components for each pixel is returned.

";

%feature("docstring")  itk::simple::Image::GetNumberOfPixels "

Get the number of pixels in the image.


To Calculate the total number of values stored continuously for the
image's buffer, the NumberOfPixels should be multiplied by
NumberOfComponentsPerPixel in order to account for multiple component
images.

";

%feature("docstring")  itk::simple::Image::GetPixelID "

Get the pixel type

The pixel type is set at construction type and can not be manually
changed, unless by assignment. The value may be -1 or \"Unknown\".

";

%feature("docstring")  itk::simple::Image::GetPixelIDTypeAsString "

Return the pixel type as a human readable string value.

";

%feature("docstring")  itk::simple::Image::GetPixelIDValue "
";

%feature("docstring")  itk::simple::Image::GetSize "

Get the number of pixels the Image is in each dimension as a std::vector. The size of the vector is
equal to the number of dimensions for the image.

";

%feature("docstring")  itk::simple::Image::GetWidth "

Get the number of pixels the Image is in the first dimension

";

%feature("docstring")  itk::simple::Image::HasMetaDataKey "

Query the meta-data dictionary for the existence of a key.

";

%feature("docstring")  itk::simple::Image::Image "

Default constructor, creates an image of size 0.

";

%feature("docstring")  itk::simple::Image::Image "
";

%feature("docstring")  itk::simple::Image::Image "

Move constructor and assignment.




Parameters:

img:
After the operation img is valid only for destructing and assignment;
all other operations have undefined behavior.



";

%feature("docstring")  itk::simple::Image::IsUnique "

Returns true if no other SimpleITK Image object refers to the same internal data structure.

";

%feature("docstring")  itk::simple::Image::MakeUnique "

Performs actually coping if needed to make object unique.


The Image class by default performs lazy coping and assignment. This method
make sure that coping actually happens to the itk::Image pointed to is only pointed to by this object.

";

%feature("docstring")  itk::simple::Image::SetMetaData "

Set an entry in the meta-data dictionary.


Replaces or creates an entry in the image's meta-data dictionary.

";

%feature("docstring")  itk::simple::Image::ToString "
";

%feature("docstring")  itk::simple::Image::TransformContinuousIndexToPhysicalPoint "

Transform continuous index to physical point

";

%feature("docstring")  itk::simple::Image::TransformIndexToPhysicalPoint "

Transform index to physical point

";

%feature("docstring")  itk::simple::Image::TransformPhysicalPointToContinuousIndex "

Transform physical point to continuous index

";

%feature("docstring")  itk::simple::Image::TransformPhysicalPointToIndex "

Transform physical point to index

";

%feature("docstring")  itk::simple::Image::~Image "
";


%feature("docstring") itk::simple::ImageFileReader "

Read an image file and return a SimpleITK Image.


The reader can handle scalar images, and vector images. Pixel types
such as RGB, RGBA are loaded as multi-component images with vector
pixel types. Additionally, tensor images are loaded with the pixel
type being a 1-d vector.

An interface is also provided to access the information from the
underlying itk::ImageIO. This information can be loaded with the
ReadImageInformation method. The information is from the itk::ImageIO
interface. In some degenerate cases reading the bulk data may produce
different results. Please see itk::ImageFileReader for more details.

Reading takes place by the ITK ImageIO factory mechanism. ITK contains
many ImageIO classes which are responsible for reading separate file
formats. By default, each ImageIO is asked if it \"can read\" the
file, and the first one which \"can read\" the format is used. The
list of available ImageIOs can be obtained using the
GetRegisteredImageIOs method. The ImageIO used can be overridden with
the SetImageIO method. This is useful in cases when multiple ImageIOs
\"can read\" the file and the user wants to select a specific IO (not
the first).


See:
 itk::simple::ReadImage for the procedural interface


C++ includes: sitkImageFileReader.h
";

%feature("docstring")  itk::simple::ImageFileReader::Execute "
";

%feature("docstring")  itk::simple::ImageFileReader::GetExtractIndex "
";

%feature("docstring")  itk::simple::ImageFileReader::GetExtractSize "
";

%feature("docstring")  itk::simple::ImageFileReader::GetFileName "
";

%feature("docstring")  itk::simple::ImageFileReader::GetMetaData "

Get the value of a meta-data dictionary entry as a string.


If the key is not in the dictionary then an exception is thrown.

String types in the dictionary are returned as their native string.
Other types are printed to string before returning.

";

%feature("docstring")  itk::simple::ImageFileReader::GetMetaDataKeys "

Get the meta-data dictionary keys.


This is only valid after successful ReadImageInformation or Execute of
this filter.

Returns a vector of keys to the key/value entries in the file's meta-
data dictionary. Iterate through with these keys to get the values.

";

%feature("docstring")  itk::simple::ImageFileReader::GetName "

return user readable name of the filter

";

%feature("docstring")  itk::simple::ImageFileReader::HasMetaDataKey "

Query a meta-data dictionary for the existence of a key.

";

%feature("docstring")  itk::simple::ImageFileReader::ImageFileReader "
";

%feature("docstring")  itk::simple::ImageFileReader::ReadImageInformation "

Read only the meta-data and image information in the file.


This method can be used to determine what the size and pixel type of
an image file is without reading the whole image. Even if SimpleITK
does not support an image of a certain dimension or type, the meta-
information can still be read.

";

%feature("docstring")  itk::simple::ImageFileReader::SetExtractIndex "

starting index from the image on disk to extract.


Missing dimensions are treated the same as 0.

/sa ExtractImageFilter

";

%feature("docstring")  itk::simple::ImageFileReader::SetExtractSize "

size of image to extract from file.


By default the reader loads the entire image, this is specified when
the size has zero length.

If specified, then the image returned from Execute will be of this size. If the ImageIO and file support reading just a
region, then the reader will perform streaming.

The dimension of the image can be reduced by specifying a dimension's
size as 0. For example a size of $[10,20,30,0,0]$ results in a 3D
image with size of $[10,20,30]$. This enables reading a 5D image into
a 3D image. If the length of the specified size is greater than the
dimension of the image file, an exception will be generated. If the
size's length is less than the image's dimension then the missing
values are assumed to be zero.

When the dimension of the image is reduced, the direction cosine
matrix will be set to the identity. However, the spacing for the
selected axis will remain. The matrix from the file can still be
obtained by ImageFileReader::GetDirection.

/sa ExtractImageFilter

";

%feature("docstring")  itk::simple::ImageFileReader::SetFileName "
";

%feature("docstring")  itk::simple::ImageFileReader::ToString "

Print ourselves to string

";

%feature("docstring")  itk::simple::ImageFileReader::~ImageFileReader "
";


%feature("docstring") itk::simple::ImageFileWriter "

Write out a SimpleITK image to the specified file location.


This writer tries to write the image out using the image's type to the
location specified in FileName. If writing fails, an ITK exception is
thrown.


See:
 itk::simple::WriteImage for the procedural interface


C++ includes: sitkImageFileWriter.h
";

%feature("docstring")  itk::simple::ImageFileWriter::Execute "
";

%feature("docstring")  itk::simple::ImageFileWriter::Execute "
";

%feature("docstring")  itk::simple::ImageFileWriter::GetFileName "
";

%feature("docstring")  itk::simple::ImageFileWriter::GetName "

return user readable name of the filter

";

%feature("docstring")  itk::simple::ImageFileWriter::GetRegisteredImageIOs "

Get a vector of the names of registered itk ImageIOs.

";

%feature("docstring")  itk::simple::ImageFileWriter::ImageFileWriter "
";

%feature("docstring")  itk::simple::ImageFileWriter::SetFileName "
";

%feature("docstring")  itk::simple::ImageFileWriter::ToString "

Print ourselves to string

";

%feature("docstring")  itk::simple::ImageFileWriter::~ImageFileWriter "
";


%feature("docstring") itk::simple::ImageFilter "

The base interface for SimpleITK filters that take one input image.


All SimpleITK filters which take one input image should inherit from
this class

C++ includes: sitkImageFilter.h
";

%feature("docstring")  itk::simple::ImageFilter::ImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ImageFilter::~ImageFilter "

Default Destructor

";


%feature("docstring") itk::simple::ImageReaderBase "

An abract base class for image readers.

C++ includes: sitkImageReaderBase.h
";

%feature("docstring")  itk::simple::ImageReaderBase::Execute "
";

%feature("docstring")  itk::simple::ImageReaderBase::GetRegisteredImageIOs "

Get a vector of the names of registered itk ImageIOs.

";

%feature("docstring")  itk::simple::ImageReaderBase::ImageReaderBase "
";

%feature("docstring")  itk::simple::ImageReaderBase::ToString "
";

%feature("docstring")  itk::simple::ImageReaderBase::~ImageReaderBase "
";


%feature("docstring") itk::simple::ImageRegistrationMethod "

An interface method to the modular ITKv4 registration framework.


This interface method class encapsulates typical registration usage by
incorporating all the necessary elements for performing a simple image
registration between two images. This method also allows for
multistage registration whereby each stage is characterized by
possibly different transforms and different image metrics. For
example, many users will want to perform a linear registration
followed by deformable registration where both stages are performed in
multiple levels. Each level can be characterized by:


the resolution of the virtual domain image (see below)

smoothing of the fixed and moving images
 Multiple stages are handled by linking multiple instantiations of
this class where the output transform is added to the optional
composite transform input.


See:
 itk::ImageRegistrationMethodv4

 itk::ImageToImageMetricv4

 itk::ObjectToObjectOptimizerBaseTemplate


C++ includes: sitkImageRegistrationMethod.h
";

%feature("docstring")  itk::simple::ImageRegistrationMethod::Execute "

Optimize the configured registration problem.

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetCurrentLevel "
";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetMetricNumberOfValidPoints "

Current number of points used of metric evaluation

This is a active measurement connected to the registration processes
during registration. This number is number of point in the virtual
domain which overlap the fixed image and the moving image. It is valid
for sparse or dense sampling. After execution of registration this
will contain the last value.

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetMetricSamplingPercentagePerLevel "

Get the percentage of pixels used for metric evaluation.

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetMetricValue "
";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetName "

return user readable name for the filter

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetOptimizerConvergenceValue "
";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetOptimizerIteration "

Active measurements which can be obtained during call backs.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetOptimizerLearningRate "
";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetOptimizerPosition "
";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetOptimizerScales "

Get the OptimizerScales.


If the scales are explicitly set then this method returns those
values. If an estimator is used then this is an active measurement
returning the scales estimated by the estimator and is only available
during execution.

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::GetOptimizerStopConditionDescription "

Measurement updated at the end of execution.

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::ImageRegistrationMethod "
";

%feature("docstring")  itk::simple::ImageRegistrationMethod::MetricEvaluate "

Get the value of the metric given the state of the method.


Passing a fixed and moving image, this method constructs and
configures a metric object to obtain the value. This will take into
consideration the current transforms, metric, interpolator, and image
masks. It does not take into consideration the sampling strategy,
smoothing sigmas, or the shrink factors.

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetInitialTransformAsBSpline "

Set an initial BSpline transform to optimize.


A specialization of SetInitialTransform for BSplineTransforms which
can take an additional scaleFactors parameter. The scaleFactors
specifies the a isotropic scaling factor per level for the BSpline
transform mesh size with respect to the initial transform. For example
to double the BSpline mesh resolution at each of 3 levels the vector
[1,2,4] should be provided.

If a per level scale factor is 0 or omitted than no transform adapter
will be created for that level.


See:
 itk::BSplineTransformParametersAdaptor


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetMetricAsANTSNeighborhoodCorrelation "

Use normalized cross correlation using a small neighborhood for each
voxel between two images, with speed optimizations for dense
registration.



See:
 itk::ANTSNeighborhoodCorrelationImageToImageMetricv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetMetricAsCorrelation "

Use negative normalized cross correlation image metric.



See:
 itk::CorrelationImageToImageMetricv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetMetricAsDemons "

Use demons image metric.



See:
 itk::DemonsImageToImageMetricv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetMetricAsJointHistogramMutualInformation "

Use mutual information between two images.



See:
 itk::JointHistogramMutualInformationImageToImageMetricv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetMetricAsMattesMutualInformation "

Use the mutual information between two images to be registered using
the method of Mattes et al.



See:
 itk::MattesMutualInformationImageToImageMetricv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetMetricAsMeanSquares "

Use negative means squares image metric.



See:
 itk::MeanSquaresImageToImageMetricv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetMetricFixedMask "

Set an image mask in order to restrict the sampled points for the
metric.


The image is expected to be in the same physical space as the
FixedImage, and if the pixel type is not UInt8 than the image will
base cast.


See:
 itk::ImageToImageMetricv4::SetFixedImageMask


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetMetricMovingMask "

Set an image mask in order to restrict the sampled points for the
metric in the moving image space.


The image is expected to be in the same physical space as the
MovingImage, and if the pixel type is not UInt8 than the image will
base cast.


See:
 itk::ImageToImageMetricv4::SetMovingImageMask


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetMetricSamplingStrategy "

Set sampling strategy for sample generation.



See:
 itk::ImageRegistrationMethodv4::SetMetricSamplingStrategy


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsAmoeba "

Set optimizer to Nelder-Mead downhill simplex algorithm.



See:
 itk::AmoebaOptimizerv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsConjugateGradientLineSearch "

Conjugate gradient descent optimizer with a golden section line search
for nonlinear optimization.



See:
 itk::ConjugateGradientLineSearchOptimizerv4Template


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsExhaustive "

Set the optimizer to sample the metric at regular steps.


At each iteration the GetOptimizerIteration, can be used to index into
the sampling grid along with the GetCurrentMetricValue.

The resulting transform and value at the end of execution is the best
location.

The OptimizerScales can be used to perform anisotropic sampling.


This optimizer is not suitable for use in conjunction with the
multiple scales.

See:
 itk::ExhaustiveOptimizerv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsGradientDescent "

Gradient descent optimizer.



See:
 itk::GradientDescentOptimizerv4Template


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsGradientDescentLineSearch "

Gradient descent optimizer with a golden section line search.



See:
 itk::GradientDescentLineSearchOptimizerv4Template


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsLBFGS2 "

Limited memory Broyden Fletcher Goldfarb Shannon minimization without
bounds.


The default parameters utilize LBFGSB in unbounded mode. This version
is from LibLBFGS.

There are upto 3 stopping criteria:
the solution accuracy which is the magnitude of the gradient

the delta convergence which ensures the decrease of the metric

maximum number of iterations



See:
 itk::LBFGS2Optimizerv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsLBFGSB "

Limited memory Broyden Fletcher Goldfarb Shannon minimization with
simple bounds.


The default parameters utilize LBFGSB in unbounded mode.


See:
 itk::LBFGSBOptimizerv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsOnePlusOneEvolutionary "

1+1 evolutionary optimizer strategy.


The seed parameter is used to seed the pseudo-random number generator.
If the seed parameter is 0, then the wall clock is used to seed,
otherwise the fixed seed is used for reproducible behavior.


See:
 itk::OnePlusOneEvolutionaryOptimizerv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsPowell "

Powell optimization using Brent line search.



See:
 itk::PowellOptimizerv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerAsRegularStepGradientDescent "

Regular Step Gradient descent optimizer.



See:
 itk::RegularStepGradientDescentOptimizerv4


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerScales "

Manually set per parameter weighting for the transform parameters.

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerScalesFromIndexShift "

Estimate scales from maximum voxel shift in index space cause by
parameter change.



See:
 itk::RegistrationParameterScalesFromIndexShift


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerScalesFromJacobian "

Estimate scales from Jacobian norms.


This scales estimator works well with versor based transforms.


See:
 itk::RegistrationParameterScalesFromJacobian


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetOptimizerScalesFromPhysicalShift "

Estimating scales of transform parameters a step sizes, from the
maximum voxel shift in physical space caused by a parameter change.



See:
 itk::RegistrationParameterScalesFromPhysicalShift


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetShrinkFactorsPerLevel "

Set the isotropic shrink factors for each level.


The virtual domain image is shrunk by this factor relative to the full
size of the original virtual domain.


See:
 itk::ImageRegistrationMethodv4::SetShrinkFactorsPerLevel


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::SetSmoothingSigmasPerLevel "

Set the sigmas of Gaussian used for smoothing.


The smoothing is applied to both the fixed and the moving images at
each level. The number of smoothing sigmas must match the number of
shrink factors.


See:
 itk::ImageRegistrationMethodv4::SetSmoothingSigmasPerLevel


";

%feature("docstring")  itk::simple::ImageRegistrationMethod::ToString "

Print the information about the object to a string.


If called when the process is being executed ( during a callback ),
the ITK Optimizer and Transform objects will be printed.

";

%feature("docstring")  itk::simple::ImageRegistrationMethod::~ImageRegistrationMethod "
";


%feature("docstring") itk::simple::ImageSeriesReader "

Read series of image files into a SimpleITK image.


For some image formats such as DICOM, images also contain associated
meta-data (e.g. imaging modality, patient name etc.). By default the
reader does not load this information (saves time). To load the meta-
data you will need to explicitly configure the reader,
MetaDataDictionaryArrayUpdateOn, and possibly specify that you also
want to load the private meta-data LoadPrivateTagsOn.

Once the image series is read the meta-data is directly accessible
from the reader.


See:
 itk::simple::ReadImage for the procedural interface


C++ includes: sitkImageSeriesReader.h
";

%feature("docstring")  itk::simple::ImageSeriesReader::Execute "
";

%feature("docstring")  itk::simple::ImageSeriesReader::GetFileNames "
";

%feature("docstring")  itk::simple::ImageSeriesReader::GetMetaData "

Get the value of a meta-data dictionary entry as a string.


If the key is not in the dictionary then an exception is thrown.

string types in the dictionary are returned as their native string.
Other types are printed to string before returning.

";

%feature("docstring")  itk::simple::ImageSeriesReader::GetMetaDataDictionaryArrayUpdate "
";

%feature("docstring")  itk::simple::ImageSeriesReader::GetMetaDataKeys "

Get the meta-data dictionary keys for a slice.


This is only valid after successful execution of this filter and when
MetaDataDictionaryArrayUpdate is true. Each element in the array
corresponds to a \"slice\" or filename read during execution.

If the slice index is out of range, an exception will be thrown.

Returns a vector of keys to the key/value entries in the file's meta-
data dictionary. Iterate through with these keys to get the values.

";

%feature("docstring")  itk::simple::ImageSeriesReader::GetName "

return user readable name of the filter

";

%feature("docstring")  itk::simple::ImageSeriesReader::HasMetaDataKey "

Query a meta-data dictionary for the existence of a key.

";

%feature("docstring")  itk::simple::ImageSeriesReader::ImageSeriesReader "
";

%feature("docstring")  itk::simple::ImageSeriesReader::MetaDataDictionaryArrayUpdateOff "
";

%feature("docstring")  itk::simple::ImageSeriesReader::MetaDataDictionaryArrayUpdateOn "

Set the value of MetaDataDictionaryArrayUpdate to true or false
respectively.

";

%feature("docstring")  itk::simple::ImageSeriesReader::SetFileNames "
";

%feature("docstring")  itk::simple::ImageSeriesReader::SetMetaDataDictionaryArrayUpdate "

Set/Get whether the meta-data dictionaries for the slices should be
read. Default value is false, because of the additional computation
time.

";

%feature("docstring")  itk::simple::ImageSeriesReader::ToString "

Print ourselves to string

";

%feature("docstring")  itk::simple::ImageSeriesReader::~ImageSeriesReader "
";


%feature("docstring") itk::simple::ImageSeriesWriter "

Writer series of image from a SimpleITK image.


The ImageSeriesWriter is for writing a 3D image as a series of 2D images. A list of names
for the series of 2D images must be provided, and an exception will be
generated if the number of file names does not match the size of the
image in the z-direction.

DICOM series cannot be written with this class, as an exception will
be generated. To write a DICOM series the individual slices must be
extracted, proper DICOM tags must be added to the dictionaries, then
written with the ImageFileWriter.


See:
 itk::simple::WriteImage for the procedural interface


C++ includes: sitkImageSeriesWriter.h
";

%feature("docstring")  itk::simple::ImageSeriesWriter::Execute "
";

%feature("docstring")  itk::simple::ImageSeriesWriter::Execute "
";

%feature("docstring")  itk::simple::ImageSeriesWriter::GetName "

return user readable name of the filter

";

%feature("docstring")  itk::simple::ImageSeriesWriter::GetRegisteredImageIOs "

Get a vector of the names of registered itk ImageIOs.

";

%feature("docstring")  itk::simple::ImageSeriesWriter::ImageSeriesWriter "
";

%feature("docstring")  itk::simple::ImageSeriesWriter::ToString "

Print ourselves to string

";

%feature("docstring")  itk::simple::ImageSeriesWriter::~ImageSeriesWriter "
";


%feature("docstring") itk::simple::ImageViewer "

Display an image in an external viewer (Fiji by default)


The ImageViewer class displays an image with an external image display application.
By default the class will search for a Fiji ( https://fiji.sc ) executable. The image is written out to a temporary file and then
passed to the application.

When the first ImageViewer object is constructed the following environment variables are queried
to set up the external viewer:

SITK_SHOW_EXTENSION: file format extension of the temporary image
file. The default is '.mha', the MetaIO file format.

SITK_SHOW_COMMAND: The user can specify an application other than Fiji
to view images.

The environment variables are not checked for subsequent ImageViewer objects.

C++ includes: sitkImageViewer.h
";

%feature("docstring")  itk::simple::ImageViewer::Execute "

Launch the viewing application to display the given image.

";

%feature("docstring")  itk::simple::ImageViewer::GetApplication "

Get the full path to the viewing application used in the command
string.

";

%feature("docstring")  itk::simple::ImageViewer::GetCommand "
";

%feature("docstring")  itk::simple::ImageViewer::GetName "

Return the user readable name of the class

";

%feature("docstring")  itk::simple::ImageViewer::ImageViewer "
";

%feature("docstring")  itk::simple::ImageViewer::SetApplication "

Set the full path to the viewing application used in the command
string.


The SetApplication method expects a full path name.

Using this method overrides the default application search.

By default, when this method is called, the command string is set to
\"%a %f\" which simply means the application path followed by the temporary image file.

";

%feature("docstring")  itk::simple::ImageViewer::SetCommand "

Set the command string used to launch the viewing application.


This command string may include the following tokens:


'a' for the image viewing application (Fiji by default)

'f' for SimpleITK's temporary image file
 For example, the default command string on Linux systems is:


After token substitution it may become:


For another example, the default command string on Mac OS X is:


After token substitution the string may become:


The string after '-eval' is an ImageJ macro the opens the file and sets the title of the
window.

If the 'f' token is not found in the command string, the temporary file name is
automatically appended to the command argument list.

Note: Using the ImageViewer::SetCommand method overrides the default command and/or the SITK_SHOW_COMMAND
environment variable.

";

%feature("docstring")  itk::simple::ImageViewer::ToString "

Print ourself out to a string.

";


%feature("docstring") itk::simple::ImportImageFilter "

Compose a 2D or 3D image and return a smart pointer to a SimpleITK
image.


This filter is intended to interface SimpleITK to other image
processing libraries and applications that may have their own
representation of an image class. It creates a SimpleITK image which
shares the bulk data buffer as what is set. SimpleITK will not
responsible to delete the buffer afterwards, and it buffer must remain
valid while in use.


See:
 itk::simple::ImportAsInt8, itk::simple::ImportAsUInt8, itk::simple::ImportAsInt16, itk::simple::ImportAsUInt16, itk::simple::ImportAsInt32, itk::simple::ImportAsUInt32, itk::simple::ImportAsInt64, itk::simple::ImportAsUInt64, itk::simple::ImportAsFloat, itk::simple::ImportAsDouble for the procedural interfaces.


C++ includes: sitkImportImageFilter.h
";

%feature("docstring")  itk::simple::ImportImageFilter::Execute "
";

%feature("docstring")  itk::simple::ImportImageFilter::GetDirection "
";

%feature("docstring")  itk::simple::ImportImageFilter::GetName "

return user readable name of the filter

";

%feature("docstring")  itk::simple::ImportImageFilter::GetOrigin "
";

%feature("docstring")  itk::simple::ImportImageFilter::GetSize "
";

%feature("docstring")  itk::simple::ImportImageFilter::GetSpacing "
";

%feature("docstring")  itk::simple::ImportImageFilter::ImportImageFilter "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsDouble "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsFloat "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsInt16 "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsInt32 "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsInt64 "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsInt8 "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsUInt16 "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsUInt32 "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsUInt64 "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetBufferAsUInt8 "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetDirection "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetOrigin "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetSize "
";

%feature("docstring")  itk::simple::ImportImageFilter::SetSpacing "
";

%feature("docstring")  itk::simple::ImportImageFilter::ToString "

Print ourselves to string

";

%feature("docstring")  itk::simple::ImportImageFilter::~ImportImageFilter "
";


%feature("docstring") itk::simple::IntensityWindowingImageFilter "

Applies a linear transformation to the intensity levels of the input Image that are inside a user-defined interval. Values below this interval
are mapped to a constant. Values over the interval are mapped to
another constant.


IntensityWindowingImageFilter applies pixel-wise a linear transformation to the intensity values of
input image pixels. The linear transformation is defined by the user
in terms of the minimum and maximum values that the output image
should have and the lower and upper limits of the intensity window of
the input image. This operation is very common in visualization, and
can also be applied as a convenient preprocessing operation for image
segmentation.

All computations are performed in the precision of the input pixel's
RealType. Before assigning the computed value to the output pixel.


See:
 RescaleIntensityImageFilter

 itk::simple::IntensityWindowing for the procedural interface

 itk::IntensityWindowingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkIntensityWindowingImageFilter.h
";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::Execute "
";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::GetOutputMaximum "

Set/Get the values of the maximum and minimum intensities of the
output image.

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::GetOutputMinimum "

Set/Get the values of the maximum and minimum intensities of the
output image.

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::GetWindowMaximum "

Set/Get the values of the maximum and minimum intensities of the input
intensity window.

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::GetWindowMinimum "

Set/Get the values of the maximum and minimum intensities of the input
intensity window.

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::IntensityWindowingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::SetOutputMaximum "

Set/Get the values of the maximum and minimum intensities of the
output image.

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::SetOutputMinimum "

Set/Get the values of the maximum and minimum intensities of the
output image.

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::SetWindowMaximum "

Set/Get the values of the maximum and minimum intensities of the input
intensity window.

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::SetWindowMinimum "

Set/Get the values of the maximum and minimum intensities of the input
intensity window.

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::IntensityWindowingImageFilter::~IntensityWindowingImageFilter "

Destructor

";


%feature("docstring") itk::simple::IntermodesThresholdImageFilter "

Threshold an image using the Intermodes Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the IntermodesThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::IntermodesThreshold for the procedural interface

 itk::IntermodesThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkIntermodesThresholdImageFilter.h
";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::IntermodesThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::IntermodesThresholdImageFilter::~IntermodesThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::InverseDeconvolutionImageFilter "

The direct linear inverse deconvolution filter.


The inverse filter is the most straightforward deconvolution method.
Considering that convolution of two images in the spatial domain is
equivalent to multiplying the Fourier transform of the two images, the
inverse filter consists of inverting the multiplication. In other
words, this filter computes the following: \\\\[ hat{F}(\\\\omega) = \\\\begin{cases} G(\\\\omega) / H(\\\\omega)
& \\\\text{if \\\\f$|H(\\\\omega)| \\\\geq \\\\epsilon\\\\f$} \\\\\\\\
0 & \\\\text{otherwise} \\\\end{cases} \\\\] where $\\\\hat{F}(\\\\omega)$ is the Fourier transform of the estimate produced by this filter, $G(\\\\omega)$ is the Fourier transform of the input blurred image, $H(\\\\omega)$ is the Fourier transform of the blurring kernel, and $\\\\epsilon$ is a constant real non-negative threshold (called
KernelZeroMagnitudeThreshold in this filter) that determines when the
magnitude of a complex number is considered zero.


Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France

Cory Quammen, The University of North Carolina at Chapel Hill

See:
 itk::simple::InverseDeconvolution for the procedural interface

 itk::InverseDeconvolutionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkInverseDeconvolutionImageFilter.h
";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::GetBoundaryCondition "
";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::GetKernelZeroMagnitudeThreshold "

Set/get the threshold value used to determine whether a frequency of
the Fourier transform of the blurring kernel is considered to be zero.
Default value is 1.0e-4.

";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::GetNormalize "
";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::GetOutputRegionMode "
";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::InverseDeconvolutionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::NormalizeOff "
";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::NormalizeOn "

Set the value of Normalize to true or false respectfully.

";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::SetBoundaryCondition "
";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::SetKernelZeroMagnitudeThreshold "

Set/get the threshold value used to determine whether a frequency of
the Fourier transform of the blurring kernel is considered to be zero.
Default value is 1.0e-4.

";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::SetNormalize "

Normalize the output image by the sum of the kernel components

";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::SetOutputRegionMode "
";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::InverseDeconvolutionImageFilter::~InverseDeconvolutionImageFilter "

Destructor

";


%feature("docstring") itk::simple::InverseDisplacementFieldImageFilter "

Computes the inverse of a displacement field.


InverseDisplacementFieldImageFilter takes a displacement field as input and computes the displacement
field that is its inverse. If the input displacement field was mapping
coordinates from a space A into a space B, the output of this filter
will map coordinates from the space B into the space A.

Given that both the input and output displacement field are
represented as discrete images with pixel type vector, the inverse
will be only an estimation and will probably not correspond to a
perfect inverse. The precision of the inverse can be improved at the
price of increasing the computation time and memory consumption in
this filter.

The method used for computing the inverse displacement field is to
subsample the input field using a regular grid and create Kerned-Base
Spline in which the reference landmarks are the coordinates of the
deformed point and the target landmarks are the negative of the
displacement vectors. The kernel-base spline is then used for
regularly sampling the output space and recover vector values for
every single pixel.

The subsampling factor used for the regular grid of the input field
will determine the number of landmarks in the KernelBased spline and
therefore it will have a dramatic effect on both the precision of
output displacement field and the computational time required for the
filter to complete the estimation. A large subsampling factor will
result in few landmarks in the KernelBased spline, therefore on fast
computation and low precision. A small subsampling factor will result
in a large number of landmarks in the KernelBased spline, therefore a
large memory consumption, long computation time and high precision for
the inverse estimation.

This filter expects both the input and output images to be of pixel
type Vector .
See:
 itk::simple::InverseDisplacementField for the procedural interface

 itk::InverseDisplacementFieldImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkInverseDisplacementFieldImageFilter.h
";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::GetOutputOrigin "

Get the output image origin.

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::GetOutputSpacing "

Get the output image spacing.

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::GetSize "

Get the size of the output image.

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::GetSubsamplingFactor "

Set/Get the factor used for subsampling the input displacement field.
A large value in this factor will produce a fast computation of the
inverse field but with low precision. A small value of this factor
will produce a precise computation of the inverse field at the price
of large memory consumption and long computational time.

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::InverseDisplacementFieldImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::SetOutputOrigin "

Set the output image origin.

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::SetOutputSpacing "

Set the output image spacing.

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::SetReferenceImage "

This methods sets the output size, origin, and direction to that of
the provided image

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::SetSize "

Set the size of the output image.

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::SetSubsamplingFactor "

Set/Get the factor used for subsampling the input displacement field.
A large value in this factor will produce a fast computation of the
inverse field but with low precision. A small value of this factor
will produce a precise computation of the inverse field at the price
of large memory consumption and long computational time.

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::InverseDisplacementFieldImageFilter::~InverseDisplacementFieldImageFilter "

Destructor

";


%feature("docstring") itk::simple::InverseFFTImageFilter "

Base class for inverse Fast Fourier Transform .


This is a base class for the \"inverse\" or \"reverse\" Discrete
Fourier Transform . This is an abstract base class: the actual implementation is
provided by the best child available on the system when the object is
created via the object factory system.

This class transforms a full complex image with Hermitian symmetry
into its real spatial domain representation. If the input does not
have Hermitian symmetry, the imaginary component is discarded.


See:
 ForwardFFTImageFilter , InverseFFTImageFilter

 itk::simple::InverseFFT for the procedural interface

 itk::InverseFFTImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkInverseFFTImageFilter.h
";

%feature("docstring")  itk::simple::InverseFFTImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::InverseFFTImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::InverseFFTImageFilter::InverseFFTImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::InverseFFTImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::InverseFFTImageFilter::~InverseFFTImageFilter "

Destructor

";


%feature("docstring") itk::simple::InvertDisplacementFieldImageFilter "

Iteratively estimate the inverse field of a displacement field.



Nick Tustison

Brian Avants

See:
 itk::simple::InvertDisplacementField for the procedural interface

 itk::InvertDisplacementFieldImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkInvertDisplacementFieldImageFilter.h
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::EnforceBoundaryConditionOff "
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::EnforceBoundaryConditionOn "

Set the value of EnforceBoundaryCondition to true or false
respectfully.

";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::GetEnforceBoundaryCondition "
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::GetMaxErrorNorm "

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::GetMaxErrorToleranceThreshold "
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::GetMaximumNumberOfIterations "
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::GetMeanErrorNorm "

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::GetMeanErrorToleranceThreshold "
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::InvertDisplacementFieldImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::SetEnforceBoundaryCondition "
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::SetMaxErrorToleranceThreshold "
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::SetMaximumNumberOfIterations "
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::SetMeanErrorToleranceThreshold "
";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::InvertDisplacementFieldImageFilter::~InvertDisplacementFieldImageFilter "

Destructor

";


%feature("docstring") itk::simple::InvertIntensityImageFilter "

Invert the intensity of an image.


InvertIntensityImageFilter inverts intensity of pixels by subtracting pixel value to a maximum
value. The maximum value can be set with SetMaximum and defaults the
maximum of input pixel type. This filter can be used to invert, for
example, a binary image, a distance map, etc.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 IntensityWindowingImageFilter ShiftScaleImageFilter

 itk::simple::InvertIntensity for the procedural interface

 itk::InvertIntensityImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkInvertIntensityImageFilter.h
";

%feature("docstring")  itk::simple::InvertIntensityImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::InvertIntensityImageFilter::Execute "
";

%feature("docstring")  itk::simple::InvertIntensityImageFilter::GetMaximum "

Set/Get the maximum intensity value for the inversion.

";

%feature("docstring")  itk::simple::InvertIntensityImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::InvertIntensityImageFilter::InvertIntensityImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::InvertIntensityImageFilter::SetMaximum "

Set/Get the maximum intensity value for the inversion.

";

%feature("docstring")  itk::simple::InvertIntensityImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::InvertIntensityImageFilter::~InvertIntensityImageFilter "

Destructor

";


%feature("docstring") itk::simple::IsoContourDistanceImageFilter "

Compute an approximate distance from an interpolated isocontour to the
close grid points.


For standard level set algorithms, it is useful to periodically
reinitialize the evolving image to prevent numerical accuracy problems
in computing derivatives. This reinitialization is done by computing a
signed distance map to the current level set. This class provides the
first step in this reinitialization by computing an estimate of the
distance from the interpolated isocontour to the pixels (or voxels)
that are close to it, i.e. for which the isocontour crosses a segment
between them and one of their direct neighbors. This class supports
narrowbanding. If the input narrowband is provided, the algorithm will
only locate the level set within the input narrowband.

Implementation of this class is based on Fast and Accurate
Redistancing for Level Set Methods Krissian K. and Westin C.F.,
EUROCAST NeuroImaging Workshop Las Palmas Spain, Ninth International
Conference on Computer Aided Systems Theory , pages 48-51, Feb 2003.
See:
 itk::simple::IsoContourDistance for the procedural interface

 itk::IsoContourDistanceImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkIsoContourDistanceImageFilter.h
";

%feature("docstring")  itk::simple::IsoContourDistanceImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::IsoContourDistanceImageFilter::GetFarValue "

Set/Get the value of the level set to be located. The default value is
0.

";

%feature("docstring")  itk::simple::IsoContourDistanceImageFilter::GetLevelSetValue "

Set/Get the value of the level set to be located. The default value is
0.

";

%feature("docstring")  itk::simple::IsoContourDistanceImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::IsoContourDistanceImageFilter::IsoContourDistanceImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::IsoContourDistanceImageFilter::SetFarValue "

Set/Get the value of the level set to be located. The default value is
0.

";

%feature("docstring")  itk::simple::IsoContourDistanceImageFilter::SetLevelSetValue "

Set/Get the value of the level set to be located. The default value is
0.

";

%feature("docstring")  itk::simple::IsoContourDistanceImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::IsoContourDistanceImageFilter::~IsoContourDistanceImageFilter "

Destructor

";


%feature("docstring") itk::simple::IsoDataThresholdImageFilter "

Threshold an image using the IsoData Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the IsoDataThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::IsoDataThreshold for the procedural interface

 itk::IsoDataThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkIsoDataThresholdImageFilter.h
";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::IsoDataThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::IsoDataThresholdImageFilter::~IsoDataThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::IsolatedConnectedImageFilter "

Label pixels that are connected to one set of seeds but not another.


IsolatedConnectedImageFilter finds the optimal threshold to separate two regions. It has two
modes, one to separate dark regions surrounded by bright regions by
automatically finding a minimum isolating upper threshold, and another
to separate bright regions surrounded by dark regions by automatically
finding a maximum lower isolating threshold. The mode can be chosen by
setting FindUpperThresholdOn() /Off(). In both cases, the isolating threshold is retrieved with GetIsolatedValue() .

The algorithm labels pixels with ReplaceValue that are connected to
Seeds1 AND NOT connected to Seeds2. When finding the threshold to
separate two dark regions surrounded by bright regions, given a fixed
lower threshold, the filter adjusts the upper threshold until the two
sets of seeds are not connected. The algorithm uses a binary search to
adjust the upper threshold, starting at Upper. The reverse is true for
finding the threshold to separate two bright regions. Lower defaults
to the smallest possible value for the InputImagePixelType, and Upper
defaults to the largest possible value for the InputImagePixelType.

The user can also supply the Lower and Upper values to restrict the
search. However, if the range is too restrictive, it could happen that
no isolating threshold can be found between the user specified Lower
and Upper values. Therefore, unless the user is sure of the bounds to
set, it is recommended that the user set these values to the lowest
and highest intensity values in the image, respectively.

The user can specify more than one seed for both regions to separate.
The algorithm will try find the threshold that ensures that all of the
first seeds are contained in the resulting segmentation and all of the
second seeds are not contained in the segmentation.

It is possible that the algorithm may not be able to find the
isolating threshold because no such threshold exists. The user can
check for this by querying the GetThresholdingFailed() flag.
See:
 itk::simple::IsolatedConnected for the procedural interface

 itk::IsolatedConnectedImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkIsolatedConnectedImageFilter.h
";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::FindUpperThresholdOff "
";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::FindUpperThresholdOn "

Set the value of FindUpperThreshold to true or false respectfully.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetFindUpperThreshold "

Set/Get whether to find an upper threshold (separating two dark
regions) or a lower threshold (separating two bright regions).

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetIsolatedValue "

Get value that isolates the two seeds.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetIsolatedValueTolerance "

Set/Get the precision required for the intensity threshold value. The
default is 1.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetLower "

Set/Get the limit on the lower threshold value. The default is the
NonpositiveMin() for the InputPixelType.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetReplaceValue "

Set/Get value to replace thresholded pixels. Pixels that lie within
the thresholds will be replaced with this value. The default is 1.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetSeed1 "
";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetSeed2 "
";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetThresholdingFailed "

Get the flag that tells whether the algorithm failed to find a
threshold.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::GetUpper "

Set/Get the limit on the upper threshold value. The default is the
max() for the InputPixelType.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::IsolatedConnectedImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::SetFindUpperThreshold "

Set/Get whether to find an upper threshold (separating two dark
regions) or a lower threshold (separating two bright regions).

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::SetIsolatedValueTolerance "

Set/Get the precision required for the intensity threshold value. The
default is 1.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::SetLower "

Set/Get the limit on the lower threshold value. The default is the
NonpositiveMin() for the InputPixelType.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::SetReplaceValue "

Set/Get value to replace thresholded pixels. Pixels that lie within
the thresholds will be replaced with this value. The default is 1.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::SetSeed1 "

DeprecatedSet seed point 1. This seed will be isolated from Seed2 (if
possible). All pixels connected to this seed will be replaced with
ReplaceValue. This method is deprecated, please use AddSeed1() .

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::SetSeed2 "

DeprecatedSet seed point 2. This seed will be isolated from Seed1 (if
possible). This method is deprecated, please use AddSeed2() .

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::SetUpper "

Set/Get the limit on the upper threshold value. The default is the
max() for the InputPixelType.

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::IsolatedConnectedImageFilter::~IsolatedConnectedImageFilter "

Destructor

";


%feature("docstring") itk::simple::IsolatedWatershedImageFilter "

Isolate watershed basins using two seeds.


IsolatedWatershedImageFilter labels pixels with ReplaceValue1 that are in the same watershed basin
as Seed1 AND NOT the same as Seed2. The filter adjusts the waterlevel
until the two seeds are not in different basins. The user supplies a
Watershed threshold. The algorithm uses a binary search to adjust the
upper waterlevel, starting at UpperValueLimit. UpperValueLimit
defaults to the 1.0.
See:
 itk::simple::IsolatedWatershed for the procedural interface

 itk::IsolatedWatershedImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkIsolatedWatershedImageFilter.h
";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::GetIsolatedValueTolerance "

Set/Get the precision required for the intensity threshold value. The
default is .001.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::GetReplaceValue1 "

Set/Get value to replace Seed1(Seed2) pixels, pixels that are within
the basin that contains Seed1(Seed2) this value. The default is 1(0).

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::GetReplaceValue2 "

Set/Get value to replace Seed1(Seed2) pixels, pixels that are within
the basin that contains Seed1(Seed2) this value. The default is 1(0).

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::GetSeed1 "

Set seed point 1. This seed will be isolated from Seed2 (if possible).
All pixels connected to this seed will be replaced with ReplaceValue1.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::GetSeed2 "

Set seed point 2. This seed will be isolated from Seed1 (if possible).
All pixels connected to this seed will be replaced with ReplaceValue2.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::GetThreshold "

Set/Get the Watershed threshold. The default is 0.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::GetUpperValueLimit "

Set/Get the limit on the upper waterlevel value. The default is 1.0.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::IsolatedWatershedImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::SetIsolatedValueTolerance "

Set/Get the precision required for the intensity threshold value. The
default is .001.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::SetReplaceValue1 "

Set/Get value to replace Seed1(Seed2) pixels, pixels that are within
the basin that contains Seed1(Seed2) this value. The default is 1(0).

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::SetReplaceValue2 "

Set/Get value to replace Seed1(Seed2) pixels, pixels that are within
the basin that contains Seed1(Seed2) this value. The default is 1(0).

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::SetSeed1 "

Set seed point 1. This seed will be isolated from Seed2 (if possible).
All pixels connected to this seed will be replaced with ReplaceValue1.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::SetSeed2 "

Set seed point 2. This seed will be isolated from Seed1 (if possible).
All pixels connected to this seed will be replaced with ReplaceValue2.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::SetThreshold "

Set/Get the Watershed threshold. The default is 0.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::SetUpperValueLimit "

Set/Get the limit on the upper waterlevel value. The default is 1.0.

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::IsolatedWatershedImageFilter::~IsolatedWatershedImageFilter "

Destructor

";


%feature("docstring") itk::simple::IterativeInverseDisplacementFieldImageFilter "

Computes the inverse of a displacement field.


IterativeInverseDisplacementFieldImageFilter takes a displacement field as input and computes the displacement
field that is its inverse. If the input displacement field was mapping
coordinates from a space A into a space B, the output of this filter
will map coordinates from the space B into the space A.

The algorithm implemented in this filter uses an iterative method for
progressively refining the values of the inverse field. Starting from
the direct field, at every pixel the direct mapping of this point is
found, and a the negative of the current displacement is stored in the
inverse field at the nearest pixel. Then, subsequent iterations verify
if any of the neighbor pixels provide a better return to the current
pixel, in which case its value is taken for updating the vector in the
inverse field.

This method was discussed in the users-list during February 2004.


Corinne Mattmann

See:
 itk::simple::IterativeInverseDisplacementField for the procedural interface

 itk::IterativeInverseDisplacementFieldImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkIterativeInverseDisplacementFieldImageFilter.h
";

%feature("docstring")  itk::simple::IterativeInverseDisplacementFieldImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::IterativeInverseDisplacementFieldImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::IterativeInverseDisplacementFieldImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::IterativeInverseDisplacementFieldImageFilter::GetStopValue "
";

%feature("docstring")  itk::simple::IterativeInverseDisplacementFieldImageFilter::IterativeInverseDisplacementFieldImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::IterativeInverseDisplacementFieldImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::IterativeInverseDisplacementFieldImageFilter::SetStopValue "
";

%feature("docstring")  itk::simple::IterativeInverseDisplacementFieldImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::IterativeInverseDisplacementFieldImageFilter::~IterativeInverseDisplacementFieldImageFilter "

Destructor

";


%feature("docstring") itk::simple::JoinSeriesImageFilter "

Join N-D images into an (N+1)-D image.


This filter is templated over the input image type and the output
image type. The pixel type of them must be the same and the input
dimension must be less than the output dimension. When the input
images are N-dimensional, they are joined in order and the size of the
N+1'th dimension of the output is same as the number of the inputs.
The spacing and the origin (where the first input is placed) for the
N+1'th dimension is specified in this filter. The output image
informations for the first N dimensions are taken from the first
input. Note that all the inputs should have the same information.


Hideaki Hiraki
 Contributed in the users list http://public.kitware.com/pipermail/insight-
users/2004-February/006542.html


See:
 itk::simple::JoinSeries for the procedural interface


C++ includes: sitkJoinSeriesImageFilter.h
";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::Execute "
";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::Execute "
";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::Execute "
";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::Execute "
";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::Execute "
";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::GetOrigin "

Set/Get origin of the new dimension

";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::GetSpacing "

Set/Get spacing of the new dimension

";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::JoinSeriesImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::SetOrigin "

Set/Get origin of the new dimension

";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::SetSpacing "

Set/Get spacing of the new dimension

";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::JoinSeriesImageFilter::~JoinSeriesImageFilter "

Destructor

";


%feature("docstring") itk::simple::KittlerIllingworthThresholdImageFilter "

Threshold an image using the KittlerIllingworth Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the KittlerIllingworthThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::KittlerIllingworthThreshold for the procedural interface

 itk::KittlerIllingworthThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkKittlerIllingworthThresholdImageFilter.h
";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::KittlerIllingworthThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::KittlerIllingworthThresholdImageFilter::~KittlerIllingworthThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelContourImageFilter "

Labels the pixels on the border of the objects in a labeled image.


LabelContourImageFilter takes a labeled image as input, where the pixels in the objects are
the pixels with a value different of the BackgroundValue. Only the
pixels on the contours of the objects are kept. The pixels not on the
border are changed to BackgroundValue. The labels of the object are
the same in the input and in the output image.

The connectivity can be changed to minimum or maximum connectivity
with SetFullyConnected() . Full connectivity produces thicker contours.

https://hdl.handle.net/1926/1352


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 BinaryContourImageFilter

 itk::simple::LabelContour for the procedural interface

 itk::LabelContourImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelContourImageFilter.h
";

%feature("docstring")  itk::simple::LabelContourImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelContourImageFilter::Execute "
";

%feature("docstring")  itk::simple::LabelContourImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::LabelContourImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::LabelContourImageFilter::GetBackgroundValue "

Set/Get the background value used to identify the objects and mark the
pixels not on the border of the objects.

";

%feature("docstring")  itk::simple::LabelContourImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff.
For objects that are 1 pixel wide, use FullyConnectedOn.


";

%feature("docstring")  itk::simple::LabelContourImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelContourImageFilter::LabelContourImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelContourImageFilter::SetBackgroundValue "

Set/Get the background value used to identify the objects and mark the
pixels not on the border of the objects.

";

%feature("docstring")  itk::simple::LabelContourImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff.
For objects that are 1 pixel wide, use FullyConnectedOn.


";

%feature("docstring")  itk::simple::LabelContourImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelContourImageFilter::~LabelContourImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelImageToLabelMapFilter "

convert a labeled image to a label collection image


LabelImageToLabelMapFilter converts a label image to a label collection image. The labels are
the same in the input and the output image.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 BinaryImageToLabelMapFilter , LabelMapToLabelImageFilter

 itk::simple::LabelImageToLabelMapFilter for the procedural interface

 itk::LabelImageToLabelMapFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelImageToLabelMapFilter.h
";

%feature("docstring")  itk::simple::LabelImageToLabelMapFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelImageToLabelMapFilter::GetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::LabelImageToLabelMapFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelImageToLabelMapFilter::LabelImageToLabelMapFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelImageToLabelMapFilter::SetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::LabelImageToLabelMapFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelImageToLabelMapFilter::~LabelImageToLabelMapFilter "

Destructor

";


%feature("docstring") itk::simple::LabelIntensityStatisticsImageFilter "

a convenient class to convert a label image to a label map and valuate
the statistics attributes at once



Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 StatisticsLabelObject , LabelStatisticsOpeningImageFilter , LabelStatisticsOpeningImageFilter

 itk::LabelImageToStatisticsLabelMapFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelIntensityStatisticsImageFilter.h
";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::ComputeFeretDiameterOff "
";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::ComputeFeretDiameterOn "

Set the value of ComputeFeretDiameter to true or false respectfully.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::ComputePerimeterOff "
";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::ComputePerimeterOn "

Set the value of ComputePerimeter to true or false respectfully.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetBoundingBox "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetCenterOfGravity "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetCentroid "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetComputeFeretDiameter "

Set/Get whether the maximum Feret diameter should be computed or not.
The defaut value is false, because of the high computation time
required.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetComputePerimeter "

Set/Get whether the perimeter should be computed or not. The defaut
value is false, because of the high computation time required.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetElongation "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetEquivalentEllipsoidDiameter "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetEquivalentSphericalPerimeter "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetEquivalentSphericalRadius "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetFeretDiameter "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetFlatness "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetKurtosis "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetLabels "

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetMaximum "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetMaximumIndex "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetMean "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetMedian "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetMinimum "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetMinimumIndex "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetNumberOfBins "

Set/Get the number of bins in the histogram. Note that the histogram
is used to compute the median value, and that this option may have an
effect on the value of the median.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetNumberOfLabels "

Return the number of labels after execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetNumberOfPixels "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetNumberOfPixelsOnBorder "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetPerimeter "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetPerimeterOnBorder "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetPerimeterOnBorderRatio "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetPhysicalSize "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetPrincipalAxes "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetPrincipalMoments "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetRegion "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetRoundness "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetSkewness "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetStandardDeviation "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetSum "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetVariance "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetWeightedElongation "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetWeightedFlatness "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetWeightedPrincipalAxes "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::GetWeightedPrincipalMoments "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::HasLabel "

Does the specified label exist? Can only be called after a call a call
to Update().

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::LabelIntensityStatisticsImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::SetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::SetComputeFeretDiameter "

Set/Get whether the maximum Feret diameter should be computed or not.
The defaut value is false, because of the high computation time
required.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::SetComputePerimeter "

Set/Get whether the perimeter should be computed or not. The defaut
value is false, because of the high computation time required.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::SetNumberOfBins "

Set/Get the number of bins in the histogram. Note that the histogram
is used to compute the median value, and that this option may have an
effect on the value of the median.

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelIntensityStatisticsImageFilter::~LabelIntensityStatisticsImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelMapContourOverlayImageFilter "

Apply a colormap to the contours (outlines) of each object in a label
map and superimpose it on top of the feature image.


The feature image is typically the image from which the labeling was
produced. Use the SetInput function to set the LabelMap , and the SetFeatureImage function to set the feature image.

Apply a colormap to a label map and put it on top of the input image.
The set of colors is a good selection of distinct colors. The opacity
of the label map can be defined by the user. A background label
produce a gray pixel with the same intensity than the input one.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 LabelMapOverlayImageFilter , LabelOverlayImageFilter , LabelOverlayFunctor

 LabelMapToBinaryImageFilter , LabelMapToLabelImageFilter ,

 itk::simple::LabelMapContourOverlay for the procedural interface

 itk::LabelMapContourOverlayImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelMapContourOverlayImageFilter.h
";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::GetColormap "
";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::GetContourThickness "

Set/Get the contour thickness - 1 by default.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::GetContourType "

Set/Get the overlay type - CONTOUR is used by default.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::GetDilationRadius "

Set/Get the object dilation radius - 0 by default.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::GetOpacity "

Set/Get the opacity of the colored label image. The value must be
between 0 and 1

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::GetPriority "

Set/Get the object priority - HIGH_LABEL_ON_TOP by default.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::GetSliceDimension "

Set/Get the slice dimension - defaults to image dimension - 1.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::LabelMapContourOverlayImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::SetColormap "
";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::SetContourThickness "

Set/Get the contour thickness - 1 by default.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::SetContourType "

Set/Get the overlay type - CONTOUR is used by default.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::SetDilationRadius "

Set/Get the object dilation radius - 0 by default.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::SetDilationRadius "

Set the values of the DilationRadius vector all to value

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::SetOpacity "

Set/Get the opacity of the colored label image. The value must be
between 0 and 1

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::SetPriority "

Set/Get the object priority - HIGH_LABEL_ON_TOP by default.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::SetSliceDimension "

Set/Get the slice dimension - defaults to image dimension - 1.

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelMapContourOverlayImageFilter::~LabelMapContourOverlayImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelMapMaskImageFilter "

Mask and image with a LabelMap .


LabelMapMaskImageFilter mask the content of an input image according to the content of the
input LabelMap . The masked pixel of the input image are set to the BackgroundValue. LabelMapMaskImageFilter can keep the input image for one label only, with Negated = false
(the default) or it can mask the input image for a single label, when
Negated equals true. In Both cases, the label is set with SetLabel() .


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 LabelMapToBinaryImageFilter , LabelMapToLabelImageFilter

 itk::simple::LabelMapMask for the procedural interface

 itk::LabelMapMaskImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelMapMaskImageFilter.h
";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::CropOff "
";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::CropOn "

Set the value of Crop to true or false respectfully.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::GetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::ZeroValue() .

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::GetCrop "

Set/Get whether the image size should be adjusted to the masked image
or not.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::GetCropBorder "

Set/Get the boder added to the mask before the crop. The default is 0
on all the axes.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::GetLabel "

The label to mask or to not mask, depending on the value of the
Negated ivar.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::GetNegated "

Set/Get whether the Label should be masked or not.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::LabelMapMaskImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::NegatedOff "
";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::NegatedOn "

Set the value of Negated to true or false respectfully.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::SetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::ZeroValue() .

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::SetCrop "

Set/Get whether the image size should be adjusted to the masked image
or not.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::SetCropBorder "

Set/Get the boder added to the mask before the crop. The default is 0
on all the axes.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::SetCropBorder "

Set the values of the CropBorder vector all to value

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::SetLabel "

The label to mask or to not mask, depending on the value of the
Negated ivar.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::SetNegated "

Set/Get whether the Label should be masked or not.

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelMapMaskImageFilter::~LabelMapMaskImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelMapOverlayImageFilter "

Apply a colormap to a label map and superimpose it on an image.


Apply a colormap to a label map and put it on top of the feature
image. The feature image is typically the image from which the
labeling was produced. Use the SetInput function to set the LabelMap , and the SetFeatureImage function to set the feature image.

The set of colors is a good selection of distinct colors. The opacity
of the label map can be defined by the user. A background label
produce a gray pixel with the same intensity than the input one.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 LabelOverlayImageFilter , LabelOverlayFunctor

 LabelMapToRGBImageFilter , LabelMapToBinaryImageFilter , LabelMapToLabelImageFilter

 itk::simple::LabelMapOverlay for the procedural interface

 itk::LabelMapOverlayImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelMapOverlayImageFilter.h
";

%feature("docstring")  itk::simple::LabelMapOverlayImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelMapOverlayImageFilter::GetColormap "
";

%feature("docstring")  itk::simple::LabelMapOverlayImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelMapOverlayImageFilter::GetOpacity "

Set/Get the opacity of the colored label image. The value must be
between 0 and 1

";

%feature("docstring")  itk::simple::LabelMapOverlayImageFilter::LabelMapOverlayImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelMapOverlayImageFilter::SetColormap "
";

%feature("docstring")  itk::simple::LabelMapOverlayImageFilter::SetOpacity "

Set/Get the opacity of the colored label image. The value must be
between 0 and 1

";

%feature("docstring")  itk::simple::LabelMapOverlayImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelMapOverlayImageFilter::~LabelMapOverlayImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelMapToBinaryImageFilter "

Convert a LabelMap to a binary image.


LabelMapToBinaryImageFilter to a binary image. All the objects in the image are used as
foreground. The background values of the original binary image can be
restored by passing this image to the filter with the
SetBackgroundImage() method.

This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 LabelMapToLabelImageFilter , LabelMapMaskImageFilter

 itk::simple::LabelMapToBinary for the procedural interface

 itk::LabelMapToBinaryImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelMapToBinaryImageFilter.h
";

%feature("docstring")  itk::simple::LabelMapToBinaryImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelMapToBinaryImageFilter::GetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::LabelMapToBinaryImageFilter::GetForegroundValue "

Set/Get the value used as \"foreground\" in the output image. Defaults
to NumericTraits<PixelType>::max() .

";

%feature("docstring")  itk::simple::LabelMapToBinaryImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelMapToBinaryImageFilter::LabelMapToBinaryImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelMapToBinaryImageFilter::SetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::LabelMapToBinaryImageFilter::SetForegroundValue "

Set/Get the value used as \"foreground\" in the output image. Defaults
to NumericTraits<PixelType>::max() .

";

%feature("docstring")  itk::simple::LabelMapToBinaryImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelMapToBinaryImageFilter::~LabelMapToBinaryImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelMapToLabelImageFilter "

Converts a LabelMap to a labeled image.


LabelMapToBinaryImageFilter to a label image.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 LabelMapToBinaryImageFilter , LabelMapMaskImageFilter

 itk::simple::LabelMapToLabel for the procedural interface

 itk::LabelMapToLabelImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelMapToLabelImageFilter.h
";

%feature("docstring")  itk::simple::LabelMapToLabelImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelMapToLabelImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelMapToLabelImageFilter::LabelMapToLabelImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelMapToLabelImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelMapToLabelImageFilter::~LabelMapToLabelImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelMapToRGBImageFilter "

Convert a LabelMap to a colored image.



Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 LabelToRGBImageFilter , LabelToRGBFunctor

 LabelMapOverlayImageFilter , LabelMapToBinaryImageFilter , LabelMapMaskImageFilter

 itk::simple::LabelMapToRGB for the procedural interface

 itk::LabelMapToRGBImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelMapToRGBImageFilter.h
";

%feature("docstring")  itk::simple::LabelMapToRGBImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelMapToRGBImageFilter::GetColormap "
";

%feature("docstring")  itk::simple::LabelMapToRGBImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelMapToRGBImageFilter::LabelMapToRGBImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelMapToRGBImageFilter::SetColormap "
";

%feature("docstring")  itk::simple::LabelMapToRGBImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelMapToRGBImageFilter::~LabelMapToRGBImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelOverlapMeasuresImageFilter "

Computes overlap measures between the set same set of labels of pixels
of two images. Background is assumed to be 0.


This code was contributed in the Insight Journal paper: \"Introducing
Dice, Jaccard, and Other Label Overlap Measures To ITK\" by Nicholas
J. Tustison, James C. Gee https://hdl.handle.net/10380/3141 http://www.insight-journal.org/browse/publication/707


Nicholas J. Tustison

See:
 LabelOverlapMeasuresImageFilter

 itk::LabelOverlapMeasuresImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelOverlapMeasuresImageFilter.h
";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetDiceCoefficient "

Get the mean overlap (Dice coefficient) for the specified individual
label.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetDiceCoefficient "

Get the mean overlap (Dice coefficient) over all labels.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetFalseNegativeError "

Get the false negative error for the specified individual label.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetFalseNegativeError "

Get the false negative error over all labels.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetFalsePositiveError "

Get the false positive error for the specified individual label.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetFalsePositiveError "

Get the false positive error over all labels.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetJaccardCoefficient "

Get the union overlap (Jaccard coefficient) for the specified
individual label.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetJaccardCoefficient "

Get the union overlap (Jaccard coefficient) over all labels.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetMeanOverlap "

Get the mean overlap (Dice coefficient) for the specified individual
label.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetMeanOverlap "

Get the mean overlap (Dice coefficient) over all labels.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetUnionOverlap "

Get the union overlap (Jaccard coefficient) for the specified
individual label.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetUnionOverlap "

Get the union overlap (Jaccard coefficient) over all labels.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetVolumeSimilarity "

Get the volume similarity for the specified individual label.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::GetVolumeSimilarity "

Get the volume similarity over all labels.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::LabelOverlapMeasuresImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelOverlapMeasuresImageFilter::~LabelOverlapMeasuresImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelOverlayImageFilter "

Apply a colormap to a label image and put it on top of the input
image.


Apply a colormap to a label image and put it on top of the input
image. The set of colors is a good selection of distinct colors. The
opacity of the label image can be defined by the user. The user can
also choose if the want to use a background and which label value is
the background. A background label produce a gray pixel with the same
intensity than the input one.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This class was contributed to the Insight Journal https://hdl.handle.net/1926/172


See:
 LabelToRGBImageFilter

 LabelMapOverlayImageFilter , LabelOverlayFunctor

 itk::simple::LabelOverlay for the procedural interface

 itk::LabelOverlayImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelOverlayImageFilter.h
";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::Execute "
";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::GetBackgroundValue "

Set/Get the background value

";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::GetColormap "
";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::GetOpacity "

Set/Get the opacity of the colored label image. The value must be
between 0 and 1

";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::LabelOverlayImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::SetBackgroundValue "

Set/Get the background value

";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::SetColormap "
";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::SetOpacity "

Set/Get the opacity of the colored label image. The value must be
between 0 and 1

";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelOverlayImageFilter::~LabelOverlayImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelShapeStatisticsImageFilter "

Converts a label image to a label map and valuates the shape
attributes.


A convenient class that converts a label image to a label map and
valuates the shape attribute at once.

This implementation was taken from the Insight Journal paper:

https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ShapeLabelObject , LabelShapeOpeningImageFilter , LabelStatisticsOpeningImageFilter

 itk::LabelImageToShapeLabelMapFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelShapeStatisticsImageFilter.h
";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::ComputeFeretDiameterOff "
";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::ComputeFeretDiameterOn "

Set the value of ComputeFeretDiameter to true or false respectfully.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::ComputeOrientedBoundingBoxOff "
";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::ComputeOrientedBoundingBoxOn "

Set the value of ComputeOrientedBoundingBox to true or false
respectfully.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::ComputePerimeterOff "
";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::ComputePerimeterOn "

Set the value of ComputePerimeter to true or false respectfully.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetBoundingBox "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetCentroid "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetComputeFeretDiameter "

Set/Get whether the maximum Feret diameter should be computed or not.
Default value is false, because of the high computation time required.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetComputeOrientedBoundingBox "

Set/Get whether the oriented bounding box should be computed or not.
Default value is false because of potential memory consumption issues
with sparse labels.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetComputePerimeter "

Set/Get whether the perimeter should be computed or not. Default value
is false, because of the high computation time required.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetElongation "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetEquivalentEllipsoidDiameter "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetEquivalentSphericalPerimeter "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetEquivalentSphericalRadius "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetFeretDiameter "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetFlatness "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetIndexes "

Get an array of indexes for pixels with the label value.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetLabels "

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetNumberOfLabels "

Return the number of labels after execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetNumberOfPixels "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetNumberOfPixelsOnBorder "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetOrientedBoundingBoxDirection "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetOrientedBoundingBoxOrigin "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetOrientedBoundingBoxSize "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetOrientedBoundingBoxVertices "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetPerimeter "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetPerimeterOnBorder "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetPerimeterOnBorderRatio "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetPhysicalSize "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetPrincipalAxes "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetPrincipalMoments "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetRegion "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetRLEIndexes "

Get an array of run-length encoding (RLE) indexes for pixels with the
label value. The array is the index of a starting line, followed by
the length repeated. The length of the array is divisible by the
image's dimension + 1. For example for a 2D image the array [ 2, 3, 2]
would encode the two indexes [2,3] and [3,3].

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::GetRoundness "

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::HasLabel "

Does the specified label exist? Can only be called after a call a call
to Update().

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::LabelShapeStatisticsImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::SetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::SetComputeFeretDiameter "

Set/Get whether the maximum Feret diameter should be computed or not.
Default value is false, because of the high computation time required.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::SetComputeOrientedBoundingBox "

Set/Get whether the oriented bounding box should be computed or not.
Default value is false because of potential memory consumption issues
with sparse labels.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::SetComputePerimeter "

Set/Get whether the perimeter should be computed or not. Default value
is false, because of the high computation time required.

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelShapeStatisticsImageFilter::~LabelShapeStatisticsImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelStatisticsImageFilter "

Given an intensity image and a label map, compute min, max, variance
and mean of the pixels associated with each label or segment.


LabelStatisticsImageFilter computes the minimum, maximum, sum, mean, median, variance and sigma
of regions of an intensity image, where the regions are defined via a
label map (a second input). The label image should be integral type.
The filter needs all of its input image. It behaves as a filter with
an input and output. Thus it can be inserted in a pipline with other
filters and the statistics will only be recomputed if a downstream
filter changes.

Optionally, the filter also computes intensity histograms on each
object. If histograms are enabled, a median intensity value can also
be computed, although its accuracy is limited to the bin width of the
histogram. If histograms are not enabled, the median returns zero.

This filter is automatically multi-threaded and can stream its input
when NumberOfStreamDivisions is set to more than
Statistics are independently computed for each streamed and threaded region then
merged.

See:
 itk::LabelStatisticsImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelStatisticsImageFilter.h
";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetBoundingBox "

Return the computed bounding box for a label. A vector of minIndex,
maxIndex pairs for each axis. The intervals include the endpoints.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetCount "

Return the number of pixels for a label.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetLabels "

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetMaximum "

Return the computed Maximum for a label.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetMean "

Return the computed Mean for a label.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetMedian "

Return the computed Median for a label. Requires histograms to be
enabled!

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetMinimum "

Return the computed Minimum for a label.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetNumberOfLabels "

Return the number of labels after execution .

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetRegion "

Return the computed region.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetSigma "

Return the computed Standard Deviation for a label.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetSum "

Return the compute Sum for a label.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetUseHistograms "
";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::GetVariance "

Return the computed Variance for a label.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::HasLabel "

Does the specified label exist? Can only be called after a call a call
to Update().

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::LabelStatisticsImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::SetUseHistograms "
";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::UseHistogramsOff "
";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::UseHistogramsOn "

Set the value of UseHistograms to true or false respectfully.

";

%feature("docstring")  itk::simple::LabelStatisticsImageFilter::~LabelStatisticsImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelToRGBImageFilter "

Apply a colormap to a label image.


Apply a colormap to a label image. The set of colors is a good
selection of distinct colors. The user can choose to use a background
value. In that case, a gray pixel with the same intensity than the
background label is produced.

This code was contributed in the Insight Journal paper: \"The
watershed transform in ITK - discussion and new developments\" by
Beare R., Lehmann G. https://hdl.handle.net/1926/202 http://www.insight-journal.org/browse/publication/92


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

See:
 LabelOverlayImageFilter

 LabelMapToRGBImageFilter , LabelToRGBFunctor, ScalarToRGBPixelFunctor

 itk::simple::LabelToRGB for the procedural interface

 itk::LabelToRGBImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelToRGBImageFilter.h
";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::Execute "
";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::GetBackgroundValue "

Set/Get the background value

";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::GetColormap "
";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::LabelToRGBImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::SetBackgroundValue "

Set/Get the background value

";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::SetColormap "
";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelToRGBImageFilter::~LabelToRGBImageFilter "

Destructor

";


%feature("docstring") itk::simple::LabelUniqueLabelMapFilter "

Make sure that the objects are not overlapping.


AttributeUniqueLabelMapFilter search the overlapping zones in the overlapping objects and keeps
only a single object on all the pixels of the image. The object to
keep is selected according to their label.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


See:
 AttributeLabelObject

 itk::simple::LabelUniqueLabelMapFilter for the procedural interface

 itk::LabelUniqueLabelMapFilter for the Doxygen on the original ITK class.


C++ includes: sitkLabelUniqueLabelMapFilter.h
";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::Execute "
";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::GetReverseOrdering "
";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::LabelUniqueLabelMapFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::ReverseOrderingOff "
";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::ReverseOrderingOn "

Set the value of ReverseOrdering to true or false respectfully.

";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::SetReverseOrdering "
";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelUniqueLabelMapFilter::~LabelUniqueLabelMapFilter "

Destructor

";


%feature("docstring") itk::simple::LabelVotingImageFilter "

This filter performs pixelwise voting among an arbitrary number of
input images, where each of them represents a segmentation of the same
scene (i.e., image).


Label voting is a simple method of classifier combination applied to image
segmentation. Typically, the accuracy of the combined segmentation
exceeds the accuracy of any of the input segmentations. Voting is
therefore commonly used as a way of boosting segmentation performance.

The use of label voting for combination of multiple segmentations is
described in

T. Rohlfing and C. R. Maurer, Jr., \"Multi-classifier framework for
atlas-based image segmentation,\" Pattern Recognition Letters, 2005.

INPUTS
All input volumes to this filter must be segmentations of an image,
that is, they must have discrete pixel values where each value
represents a different segmented object.
 Input volumes must all contain the same size RequestedRegions. Not all input images must contain all possible labels, but all label
values must have the same meaning in all images.

OUTPUTS
The voting filter produces a single output volume. Each output pixel
contains the label that occurred most often among the labels assigned
to this pixel in all the input volumes, that is, the label that
received the maximum number of \"votes\" from the input pixels.. If
the maximum number of votes is not unique, i.e., if more than one
label have a maximum number of votes, an \"undecided\" label is
assigned to that output pixel.
 By default, the label used for undecided pixels is the maximum label
value used in the input images plus one. Since it is possible for an
image with 8 bit pixel values to use all 256 possible label values, it
is permissible to combine 8 bit (i.e., byte) images into a 16 bit
(i.e., short) output image.

PARAMETERS
The label used for \"undecided\" labels can be set using
SetLabelForUndecidedPixels. This functionality can be unset by calling
UnsetLabelForUndecidedPixels.

Torsten Rohlfing, SRI International, Neuroscience Program

See:
 itk::simple::LabelVoting for the procedural interface


C++ includes: sitkLabelVotingImageFilter.h
";

%feature("docstring")  itk::simple::LabelVotingImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::LabelVotingImageFilter::Execute "
";

%feature("docstring")  itk::simple::LabelVotingImageFilter::Execute "
";

%feature("docstring")  itk::simple::LabelVotingImageFilter::Execute "
";

%feature("docstring")  itk::simple::LabelVotingImageFilter::Execute "
";

%feature("docstring")  itk::simple::LabelVotingImageFilter::Execute "
";

%feature("docstring")  itk::simple::LabelVotingImageFilter::GetLabelForUndecidedPixels "

Get label value used for undecided pixels. After updating the filter,
this function returns the actual label value used for undecided pixels
in the current output. Note that this value is overwritten when
SetLabelForUndecidedPixels is called and the new value only becomes
effective upon the next filter update.

";

%feature("docstring")  itk::simple::LabelVotingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LabelVotingImageFilter::LabelVotingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LabelVotingImageFilter::SetLabelForUndecidedPixels "

Set label value for undecided pixels.

";

%feature("docstring")  itk::simple::LabelVotingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LabelVotingImageFilter::~LabelVotingImageFilter "

Destructor

";


%feature("docstring") itk::simple::LandmarkBasedTransformInitializerFilter "

This class computes the transform that aligns the fixed and moving
images given a set of pair landmarks. The class is templated over the Transform type as well as fixed image and moving image types. The transform
computed gives the best fit transform that maps the fixed and moving
images in a least squares sense. The indices are taken to correspond,
so point 1 in the first set will get mapped close to point 1 in the
second set, etc.

Currently, the following transforms are supported by the class: VersorRigid3DTransform Rigid2DTransform AffineTransform BSplineTransform

An equal number of fixed and moving landmarks need to be specified
using SetFixedLandmarks() and SetMovingLandmarks() . Any number of landmarks may be specified. In the case of using
Affine or BSpline transforms, each landmark pair can contribute in the
final transform based on its defined weight. Number of weights should
be equal to the number of landmarks and can be specified using SetLandmarkWeight() . By defaults are weights are set to one. Call InitializeTransform()
to initialize the transform.

The class is based in part on Hybrid/vtkLandmarkTransform originally
implemented in python by David G. Gobbi.

The solution is based on Berthold K. P. Horn (1987), \"Closed-form
solution of absolute orientation using unit quaternions,\" http://people.csail.mit.edu/bkph/papers/Absolute_Orientation.pdf

The Affine Transform initializer is based on an algorithm by H Spaeth, and is described in
the Insight Journal Article \"Affine Transformation for Landmark Based
Registration Initializer in ITK\" by Kim E.Y., Johnson H., Williams N.
available at http://midasjournal.com/browse/publication/825

Wiki Examples:

All Examples

Rigidly register one image to another using manually specified
landmarks
See:
 itk::simple::LandmarkBasedTransformInitializerFilter for the procedural interface

 itk::LandmarkBasedTransformInitializer for the Doxygen on the original ITK class.



C++ includes: sitkLandmarkBasedTransformInitializerFilter.h
";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::GetBSplineNumberOfControlPoints "

Set/Get the number of control points

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::GetFixedLandmarks "
";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::GetLandmarkWeight "
";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::GetMovingLandmarks "

Get the shrink factors.

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::GetReferenceImage "
";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::LandmarkBasedTransformInitializerFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::SetBSplineNumberOfControlPoints "

Set/Get the number of control points

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::SetFixedLandmarks "

Set the Fixed landmark point containers

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::SetLandmarkWeight "

Set the landmark weight point containers Weight includes diagonal
elements of weight matrix

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::SetMovingLandmarks "

Set the Moving landmark point containers

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::SetReferenceImage "

Set the reference image to define the parametric domain for the
BSpline transform

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializerFilter::~LandmarkBasedTransformInitializerFilter "

Destructor

";


%feature("docstring") itk::simple::LandweberDeconvolutionImageFilter "

Deconvolve an image using the Landweber deconvolution algorithm.


This filter implements the Landweber deconvolution algorthim as
defined in Bertero M and Boccacci P, \"Introduction to Inverse
Problems in Imaging\", 1998. The algorithm assumes that the input
image has been formed by a linear shift-invariant system with a known
kernel.

The Landweber algorithm converges to a solution that minimizes the sum
of squared errors $||f \\\\otimes h - g||$ where $f$ is the estimate of the unblurred image, $\\\\otimes$ is the convolution operator, $h$ is the blurring kernel, and $g$ is the blurred input image. As such, it is best suited for images
that have zero-mean Gaussian white noise.

This is the base implementation of the Landweber algorithm. It may
produce results with negative values. For a version of this algorithm
that enforces a positivity constraint on each intermediate solution,
see ProjectedLandweberDeconvolutionImageFilter .

This code was adapted from the Insight Journal contribution:

\"Deconvolution: infrastructure and reference algorithms\" by Gaetan
Lehmann https://hdl.handle.net/10380/3207


Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France

Cory Quammen, The University of North Carolina at Chapel Hill

See:
 IterativeDeconvolutionImageFilter

 RichardsonLucyDeconvolutionImageFilter

 ProjectedLandweberDeconvolutionImageFilter

 itk::simple::LandweberDeconvolution for the procedural interface

 itk::LandweberDeconvolutionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLandweberDeconvolutionImageFilter.h
";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::GetAlpha "

Set/get relaxation factor.

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::GetBoundaryCondition "
";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::GetNormalize "
";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::GetNumberOfIterations "

Get the number of iterations.

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::GetOutputRegionMode "
";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::LandweberDeconvolutionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::NormalizeOff "
";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::NormalizeOn "

Set the value of Normalize to true or false respectfully.

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::SetAlpha "

Set/get relaxation factor.

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::SetBoundaryCondition "
";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::SetNormalize "

Normalize the output image by the sum of the kernel components

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::SetNumberOfIterations "

Set the number of iterations.

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::SetOutputRegionMode "
";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LandweberDeconvolutionImageFilter::~LandweberDeconvolutionImageFilter "

Destructor

";


%feature("docstring") itk::simple::LaplacianImageFilter "

This filter computes the Laplacian of a scalar-valued image. The
Laplacian is an isotropic measure of the 2nd spatial derivative of an
image. The Laplacian of an image highlights regions of rapid intensity
change and is therefore often used for edge detection. Often, the
Laplacian is applied to an image that has first been smoothed with a
Gaussian filter in order to reduce its sensitivity to noise.


The Laplacian at each pixel location is computed by convolution with
the itk::LaplacianOperator .
Inputs and Outputs
The input to this filter is a scalar-valued itk::Image of arbitrary dimension. The output is a scalar-valued itk::Image .

WARNING:
The pixel type of the input and output images must be of real type
(float or double). ConceptChecking is used here to enforce the input
pixel type. You will get a compilation error if the pixel type of the
input and output images is not float or double.

See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 LaplacianOperator

 itk::simple::Laplacian for the procedural interface

 itk::LaplacianImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLaplacianImageFilter.h
";

%feature("docstring")  itk::simple::LaplacianImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LaplacianImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LaplacianImageFilter::GetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::LaplacianImageFilter::LaplacianImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LaplacianImageFilter::SetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::LaplacianImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LaplacianImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::LaplacianImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::LaplacianImageFilter::~LaplacianImageFilter "

Destructor

";


%feature("docstring") itk::simple::LaplacianRecursiveGaussianImageFilter "

Computes the Laplacian of Gaussian (LoG) of an image.


Computes the Laplacian of Gaussian (LoG) of an image by convolution
with the second derivative of a Gaussian. This filter is implemented
using the recursive gaussian filters.
See:
 itk::simple::LaplacianRecursiveGaussian for the procedural interface

 itk::LaplacianRecursiveGaussianImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLaplacianRecursiveGaussianImageFilter.h
";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::GetNormalizeAcrossScale "

Define which normalization factor will be used for the Gaussian
See:
 RecursiveGaussianImageFilter::SetNormalizeAcrossScale


";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::GetSigma "

Set Sigma value. Sigma is measured in the units of image spacing.

";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::LaplacianRecursiveGaussianImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::NormalizeAcrossScaleOff "
";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::NormalizeAcrossScaleOn "

Set the value of NormalizeAcrossScale to true or false respectfully.

";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::SetNormalizeAcrossScale "

Define which normalization factor will be used for the Gaussian
See:
 RecursiveGaussianImageFilter::SetNormalizeAcrossScale


";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::SetSigma "

Set Sigma value. Sigma is measured in the units of image spacing.

";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LaplacianRecursiveGaussianImageFilter::~LaplacianRecursiveGaussianImageFilter "

Destructor

";


%feature("docstring") itk::simple::LaplacianSegmentationLevelSetImageFilter "

Segments structures in images based on a second derivative image
features.


IMPORTANT
The SegmentationLevelSetImageFilter class and the LaplacianSegmentationLevelSetFunction class contain additional information necessary to the full
understanding of how to use this filter.
OVERVIEW
This class is a level set method segmentation filter. It constructs a
speed function which is zero at image edges as detected by a Laplacian
filter. The evolving level set front will therefore tend to lock onto
zero crossings in the image. The level set front moves fastest near
edges.

The Laplacian segmentation filter is intended primarily as a tool for
refining existing segmentations. The initial isosurface (as given in
the seed input image) should ideally be very close to the segmentation
boundary of interest. The idea is that a rough segmentation can be
refined by allowing the isosurface to deform slightly to achieve a
better fit to the edge features of an image. One example of such an
application is to refine the output of a hand segmented image.

Because values in the Laplacian feature image will tend to be low
except near edge features, this filter is not effective for segmenting
large image regions from small seed surfaces.
INPUTS
This filter requires two inputs. The first input is a seed image. This
seed image must contain an isosurface that you want to use as the seed
for your segmentation. It can be a binary, graylevel, or floating
point image. The only requirement is that it contain a closed
isosurface that you will identify as the seed by setting the
IsosurfaceValue parameter of the filter. For a binary image you will
want to set your isosurface value halfway between your on and off
values (i.e. for 0's and 1's, use an isosurface value of 0.5).

The second input is the feature image. This is the image from which
the speed function will be calculated. For most applications, this is
the image that you want to segment. The desired isosurface in your
seed image should lie within the region of your feature image that you
are trying to segment.
 Note that this filter does no preprocessing of the feature image
before thresholding. Because second derivative calculations are highly
sensitive to noise, isotropic or anisotropic smoothing of the feature
image can dramatically improve the results.


See SegmentationLevelSetImageFilter for more information on Inputs.
OUTPUTS
The filter outputs a single, scalar, real-valued image. Positive
*values in the output image are inside the segmented region and
negative *values in the image are outside of the inside region. The
zero crossings of *the image correspond to the position of the level
set front.

See SparseFieldLevelSetImageFilter and SegmentationLevelSetImageFilter for more information.
PARAMETERS
This filter has no parameters other than those described in SegmentationLevelSetImageFilter .

See:
 SegmentationLevelSetImageFilter

 LaplacianSegmentationLevelSetFunction ,

 SparseFieldLevelSetImageFilter

 itk::simple::LaplacianSegmentationLevelSet for the procedural interface

 itk::LaplacianSegmentationLevelSetImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLaplacianSegmentationLevelSetImageFilter.h
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::Execute "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::GetCurvatureScaling "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::GetElapsedIterations "

Number of iterations run.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::GetPropagationScaling "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::GetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::GetRMSChange "

The Root Mean Square of the levelset upon termination.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::LaplacianSegmentationLevelSetImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::ReverseExpansionDirectionOff "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::ReverseExpansionDirectionOn "

Set the value of ReverseExpansionDirection to true or false
respectfully.

";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::SetCurvatureScaling "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::SetPropagationScaling "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::SetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LaplacianSegmentationLevelSetImageFilter::~LaplacianSegmentationLevelSetImageFilter "

Destructor

";


%feature("docstring") itk::simple::LaplacianSharpeningImageFilter "

This filter sharpens an image using a Laplacian. LaplacianSharpening
highlights regions of rapid intensity change and therefore highlights
or enhances the edges. The result is an image that appears more in
focus.


The LaplacianSharpening at each pixel location is computed by
convolution with the itk::LaplacianOperator .
Inputs and Outputs
The input to this filter is a scalar-valued itk::Image of arbitrary dimension. The output is a scalar-valued itk::Image .

See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 LaplacianOperator

 itk::simple::LaplacianSharpening for the procedural interface

 itk::LaplacianSharpeningImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLaplacianSharpeningImageFilter.h
";

%feature("docstring")  itk::simple::LaplacianSharpeningImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LaplacianSharpeningImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LaplacianSharpeningImageFilter::GetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::LaplacianSharpeningImageFilter::LaplacianSharpeningImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LaplacianSharpeningImageFilter::SetUseImageSpacing "

Set/Get whether or not the filter will use the spacing of the input
image in its calculations

";

%feature("docstring")  itk::simple::LaplacianSharpeningImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LaplacianSharpeningImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::LaplacianSharpeningImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::LaplacianSharpeningImageFilter::~LaplacianSharpeningImageFilter "

Destructor

";


%feature("docstring") itk::simple::LessEqualImageFilter "

Implements pixel-wise generic operation of two images, or of an image
and a constant.


This class is parameterized over the types of the two input images and
the type of the output image. It is also parameterized by the
operation to be applied. A Functor style is used.

The constant must be of the same type than the pixel type of the
corresponding image. It is wrapped in a SimpleDataObjectDecorator so it can be updated through the pipeline. The SetConstant() and
GetConstant() methods are provided as shortcuts to set or get the
constant value without manipulating the decorator.


See:
 BinaryGeneratorImagFilter

 UnaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::LessEqual for the procedural interface

 itk::BinaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLessEqualImageFilter.h
";

%feature("docstring")  itk::simple::LessEqualImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::LessEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::LessEqualImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::LessEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::LessEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::LessEqualImageFilter::Execute "

Execute the filter on an image and a constant with the given
parameters

";

%feature("docstring")  itk::simple::LessEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::LessEqualImageFilter::GetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::LessEqualImageFilter::GetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::LessEqualImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LessEqualImageFilter::LessEqualImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LessEqualImageFilter::SetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::LessEqualImageFilter::SetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::LessEqualImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LessEqualImageFilter::~LessEqualImageFilter "

Destructor

";


%feature("docstring") itk::simple::LessImageFilter "

Implements pixel-wise generic operation of two images, or of an image
and a constant.


This class is parameterized over the types of the two input images and
the type of the output image. It is also parameterized by the
operation to be applied. A Functor style is used.

The constant must be of the same type than the pixel type of the
corresponding image. It is wrapped in a SimpleDataObjectDecorator so it can be updated through the pipeline. The SetConstant() and
GetConstant() methods are provided as shortcuts to set or get the
constant value without manipulating the decorator.


See:
 BinaryGeneratorImagFilter

 UnaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::Less for the procedural interface

 itk::BinaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLessImageFilter.h
";

%feature("docstring")  itk::simple::LessImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::LessImageFilter::Execute "
";

%feature("docstring")  itk::simple::LessImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::LessImageFilter::Execute "
";

%feature("docstring")  itk::simple::LessImageFilter::Execute "
";

%feature("docstring")  itk::simple::LessImageFilter::Execute "

Execute the filter on an image and a constant with the given
parameters

";

%feature("docstring")  itk::simple::LessImageFilter::Execute "
";

%feature("docstring")  itk::simple::LessImageFilter::GetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::LessImageFilter::GetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::LessImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LessImageFilter::LessImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LessImageFilter::SetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::LessImageFilter::SetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::LessImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LessImageFilter::~LessImageFilter "

Destructor

";


%feature("docstring") itk::simple::LevelSetMotionRegistrationFilter "

Deformably register two images using level set motion.


LevelSetMotionFilter implements a deformable registration algorithm
that aligns a fixed and a moving image under level set motion. The
equations of motion are similar to those of the DemonsRegistrationFilter . The main differences are: (1) Gradients of the moving image are
calculated on a smoothed image while intensity difference are measured
on the original images (2) Magnitude of the motion vector is a
function of the differences in intensity between the fixed and moving
pixel. An adaptive timestep is calculated based on the maximum motion
vector over the entire field to ensure stability. The timestep also
implicitly converts the motion vector measured in units of intensity
to a vector measured in physical units. Demons, on the other hand,
defines its motion vectors as function of both the intensity
differences and gradient magnitude at each respective pixel. Consider
two separate pixels with the same intensity differences between the
corresponding fixed and moving pixel pairs. In demons, the motion
vector of the pixel over a low gradient region will be larger than the
motion vector of the pixel over a large gradient region. This leads to
an unstable vector field. In the levelset approach, the motion vectors
will be proportional to the gradients, scaled by the maximum gradient
over the entire field. The pixel with at the lower gradient position
will more less than the pixel at the higher gradient position. (3)
Gradients are calculated using minmod finite difference instead of
using central differences.

A deformation field is represented as a image whose pixel type is some
vector type with at least N elements, where N is the dimension of the
fixed image. The vector type must support element access via operator
[]. It is assumed that the vector elements behave like floating point
scalars.

This class is templated over the fixed image type, moving image type
and the deformation field type.

The input fixed and moving images are set via methods SetFixedImage
and SetMovingImage respectively. An initial deformation field maybe
set via SetInitialDisplacementField or SetInput. If no initial field
is set, a zero field is used as the initial condition.

The algorithm has one parameters: the number of iteration to be
performed.

The output deformation field can be obtained via methods GetOutput or
GetDisplacementField.

This class make use of the finite difference solver hierarchy. Update
for each iteration is computed in LevelSetMotionFunction.


WARNING:
This filter assumes that the fixed image type, moving image type and
deformation field type all have the same number of dimensions.
 Ref: B.C. Vemuri, J. Ye, Y. Chen, C.M. Leonard. \"Image registration
via level-set motion: applications to atlas-based segmentation\".
Medical Image Analysis. Vol. 7. pp. 1-20. 2003.


See:
 LevelSetMotionRegistrationFunction

 DemonsRegistrationFilter

 itk::LevelSetMotionRegistrationFilter for the Doxygen on the original ITK class.


C++ includes: sitkLevelSetMotionRegistrationFilter.h
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::Execute "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetAlpha "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetElapsedIterations "

Number of iterations run.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetGradientMagnitudeThreshold "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetGradientSmoothingStandardDeviations "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetIntensityDifferenceThreshold "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetMetric "

Get the metric value. The metric value is the mean square difference
in intensity between the fixed image and transforming moving image
computed over the the overlapping region between the two images. This
is value is only available for the previous iteration and NOT the
current iteration.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetRMSChange "

The Root Mean Square of the levelset upon termination.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::GetUseImageSpacing "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::LevelSetMotionRegistrationFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetAlpha "

Set/Get the parameter alpha. Alpha is added to the calculated gradient
magnitude prior to normalizing the gradient to protect against
numerical instability as the gradient magnitude approaches zero. This
should be set as a small fraction of the intensity dynamic range, for
instance 0.04%. Default is the absolute (not percentage) value of 0.1.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetGradientMagnitudeThreshold "

Set/Get the threshold below which the gradient magnitude is considered
the zero vector. Default is 1e-9.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetGradientSmoothingStandardDeviations "

Set/Get the standard deviation used for smoothing the moving image
prior to calculating gradients. The standard deviation is measured in
physical units (for instance mm). Note that this smoothing value is
not to be confused with the PDEDeformableRegistrationFilter::SetStandardDeviations() method. The method in PDEDeformableRegistrationFilter is for setting the smoothing parameters for regularizing the
deformation field between iterations. Those smoothing parameters are
set in pixel units not physical units. Deformation field smoothing is
not done by default in LevelSetMotionRegistration. This smoothing
parameter is to condition the gradient calculation and parameter is
specified in physical units.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetIntensityDifferenceThreshold "

Set/Get the threshold below which the absolute difference of intensity
yields a match. When the intensities match between a moving and fixed
image pixel, the update vector (for that iteration) will be the zero
vector. Default is 0.001.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetStandardDeviations "

Set the values of the StandardDeviations vector all to value

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the values of the UpdateFieldStandardDeviations vector all to
value

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SetUseImageSpacing "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SmoothDisplacementFieldOff "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SmoothDisplacementFieldOn "

Set the value of SmoothDisplacementField to true or false
respectfully.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SmoothUpdateFieldOff "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::SmoothUpdateFieldOn "

Set the value of SmoothUpdateField to true or false respectfully.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::LevelSetMotionRegistrationFilter::~LevelSetMotionRegistrationFilter "

Destructor

";


%feature("docstring") itk::simple::LiThresholdImageFilter "

Threshold an image using the Li Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the LiThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::LiThreshold for the procedural interface

 itk::LiThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLiThresholdImageFilter.h
";

%feature("docstring")  itk::simple::LiThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::LiThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::LiThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::LiThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::LiThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::LiThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::LiThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LiThresholdImageFilter::~LiThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::Log10ImageFilter "

Computes the log10 of each pixel.


The computation is performed using std::log10(x).
See:
 itk::simple::Log10 for the procedural interface

 itk::Log10ImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLog10ImageFilter.h
";

%feature("docstring")  itk::simple::Log10ImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::Log10ImageFilter::Execute "
";

%feature("docstring")  itk::simple::Log10ImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::Log10ImageFilter::Log10ImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::Log10ImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::Log10ImageFilter::~Log10ImageFilter "

Destructor

";


%feature("docstring") itk::simple::LogImageFilter "

Computes the log() of each pixel.



See:
 itk::simple::Log for the procedural interface

 itk::LogImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkLogImageFilter.h
";

%feature("docstring")  itk::simple::LogImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::LogImageFilter::Execute "
";

%feature("docstring")  itk::simple::LogImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::LogImageFilter::LogImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::LogImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::LogImageFilter::~LogImageFilter "

Destructor

";


%feature("docstring") itk::simple::MagnitudeAndPhaseToComplexImageFilter "

Implements pixel-wise conversion of magnitude and phase data into
complex voxels.


This filter is parameterized over the types of the two input images
and the type of the output image.

The filter expect all images to have the same dimension (e.g. all 2D,
or all 3D, or all ND)
See:
 itk::simple::MagnitudeAndPhaseToComplex for the procedural interface

 itk::MagnitudeAndPhaseToComplexImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMagnitudeAndPhaseToComplexImageFilter.h
";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplexImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplexImageFilter::Execute "
";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplexImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplexImageFilter::Execute "
";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplexImageFilter::Execute "
";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplexImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplexImageFilter::MagnitudeAndPhaseToComplexImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplexImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplexImageFilter::~MagnitudeAndPhaseToComplexImageFilter "

Destructor

";


%feature("docstring") itk::simple::MaskImageFilter "

Mask an image with a mask.


This class is templated over the types of the input image type, the
mask image type and the type of the output image. Numeric conversions
(castings) are done by the C++ defaults.

The pixel type of the input 2 image must have a valid definition of
the operator != with zero. This condition is required because
internally this filter will perform the operation


The pixel from the input 1 is cast to the pixel type of the output
image.

Note that the input and the mask images must be of the same size.


WARNING:
Any pixel value other than masking value (0 by default) will not be
masked out.

See:
 MaskNegatedImageFilter

 itk::simple::Mask for the procedural interface

 itk::MaskImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMaskImageFilter.h
";

%feature("docstring")  itk::simple::MaskImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MaskImageFilter::Execute "
";

%feature("docstring")  itk::simple::MaskImageFilter::GetMaskingValue "

Method to get the masking value of the mask.

";

%feature("docstring")  itk::simple::MaskImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MaskImageFilter::GetOutsideValue "
";

%feature("docstring")  itk::simple::MaskImageFilter::MaskImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MaskImageFilter::SetMaskingValue "

Method to explicitly set the masking value of the mask. Defaults to 0

";

%feature("docstring")  itk::simple::MaskImageFilter::SetOutsideValue "

Method to explicitly set the outside value of the mask. Defaults to 0

";

%feature("docstring")  itk::simple::MaskImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MaskImageFilter::~MaskImageFilter "

Destructor

";


%feature("docstring") itk::simple::MaskNegatedImageFilter "

Mask an image with the negation (or logical compliment) of a mask.


This class is templated over the types of the input image type, the
mask image type and the type of the output image. Numeric conversions
(castings) are done by the C++ defaults.

The pixel type of the input 2 image must have a valid definition of
the operator!=. This condition is required because internally this
filter will perform the operation


The pixel from the input 1 is cast to the pixel type of the output
image.

Note that the input and the mask images must be of the same size.


WARNING:
Only pixel value with mask_value ( defaults to 0 ) will be preserved.

See:
 MaskImageFilter

 itk::simple::MaskNegated for the procedural interface

 itk::MaskNegatedImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMaskNegatedImageFilter.h
";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::Execute "
";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::GetMaskingValue "

Method to get the masking value of the mask.

";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::GetOutsideValue "
";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::MaskNegatedImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::SetMaskingValue "

Method to explicitly set the masking value of the mask. Defaults to 0

";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::SetOutsideValue "

Method to explicitly set the outside value of the mask. Defaults to 0

";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MaskNegatedImageFilter::~MaskNegatedImageFilter "

Destructor

";


%feature("docstring") itk::simple::MaskedFFTNormalizedCorrelationImageFilter "

Calculate masked normalized cross correlation using FFTs.


This filter calculates the masked normalized cross correlation (NCC)
of two images under masks using FFTs instead of spatial correlation.
It is much faster than spatial correlation for reasonably large
structuring elements. This filter is not equivalent to simply masking
the images first and then correlating them; the latter approach yields
incorrect results because the zeros in the images still affect the
metric in the correlation process. This filter implements the masked
NCC correctly so that the masked-out regions are completely ignored.
The fundamental difference is described in detail in the references
below. If the masks are set to images of all ones, the result of this
filter is the same as standard NCC.

Inputs: Two images are required as inputs, fixedImage and movingImage,
and two are optional, fixedMask and movingMask. In the context of
correlation, inputs are often defined as: \"image\" and \"template\".
In this filter, the fixedImage plays the role of the image, and the
movingImage plays the role of the template. However, this filter is
capable of correlating any two images and is not restricted to small
movingImages (templates). In the fixedMask and movingMask, non-zero
positive values indicate locations of useful information in the
corresponding image, whereas zero and negative values indicate
locations that should be masked out (ignored). Internally, the masks
are converted to have values of only 0 and 1. For each optional mask
that is not set, the filter internally creates an image of ones, which
is equivalent to not masking the image. Thus, if both masks are not
set, the result will be equivalent to unmasked NCC. For example, if
only a mask for the fixed image is needed, the movingMask can either
not be set or can be set to an image of ones.

Optional parameters: The RequiredNumberOfOverlappingPixels enables the
user to specify the minimum number of voxels of the two masks that
must overlap; any location in the correlation map that results from
fewer than this number of voxels will be set to zero. Larger values
zero-out pixels on a larger border around the correlation image. Thus,
larger values remove less stable computations but also limit the
capture range. If RequiredNumberOfOverlappingPixels is set to 0, the
default, no zeroing will take place.

The RequiredFractionOfOverlappingPixels enables the user to specify a
fraction of the maximum number of overlapping pixels that need to
overlap; any location in the correlation map that results from fewer
than the product of this fraction and the internally computed maximum
number of overlapping pixels will be set to zero. The value ranges
between 0.0 and 1.0. This is very useful when the user does does not
know beforehand the maximum number of pixels of the masks that will
overlap. For example, when the masks have strange shapes, it is
difficult to predict how the correlation of the masks will interact
and what the maximum overlap will be. It is also useful when the mask
shapes or sizes change because it is relative to the internally
computed maximum of the overlap. Larger values zero-out pixels on a
larger border around the correlation image. Thus, larger values remove
less stable computations but also limit the capture range. Experiments
have shown that a value between 0.1 and 0.6 works well for images with
significant overlap and between 0.05 and 0.1 for images with little
overlap (such as in stitching applications). If
RequiredFractionOfOverlappingPixels is set to 0, the default, no
zeroing will take place.

The user can either specify RequiredNumberOfOverlappingPixels or
RequiredFractionOfOverlappingPixels (or both or none). Internally, the
number of required pixels resulting from both of these methods is
calculated and the one that gives the largest number of pixels is
chosen. Since these both default to 0, if a user only sets one, the
other is ignored.

Image size: fixedImage and movingImage need not be the same size, but
fixedMask must be the same size as fixedImage, and movingMask must be
the same size as movingImage. Furthermore, whereas some algorithms
require that the \"template\" be smaller than the \"image\" because of
errors in the regions where the two are not fully overlapping, this
filter has no such restriction.

Image spacing: Since the computations are done in the pixel domain, all
input images must have the same spacing.

Outputs; The output is an image of RealPixelType that is the masked
NCC of the two images and its values range from -1.0 to 1.0. The size
of this NCC image is, by definition, size(fixedImage) +
size(movingImage) - 1.

Example filter usage:


WARNING:
The pixel type of the output image must be of real type (float or
double). ConceptChecking is used to enforce the output pixel type. You
will get a compilation error if the pixel type of the output image is
not float or double.
 References: 1) D. Padfield. \"Masked object registration in the
Fourier domain.\" Transactions on Image Processing. 2) D. Padfield. \"Masked FFT registration\". In Proc.
Computer Vision and Pattern Recognition, 2010.


: Dirk Padfield, GE Global Research, padfield@research.ge.com

See:
 itk::simple::MaskedFFTNormalizedCorrelation for the procedural interface

 itk::MaskedFFTNormalizedCorrelationImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMaskedFFTNormalizedCorrelationImageFilter.h
";

%feature("docstring")  itk::simple::MaskedFFTNormalizedCorrelationImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MaskedFFTNormalizedCorrelationImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MaskedFFTNormalizedCorrelationImageFilter::GetRequiredFractionOfOverlappingPixels "

Set and get the required fraction of overlapping pixels

";

%feature("docstring")  itk::simple::MaskedFFTNormalizedCorrelationImageFilter::GetRequiredNumberOfOverlappingPixels "

Set and get the required number of overlapping pixels

";

%feature("docstring")  itk::simple::MaskedFFTNormalizedCorrelationImageFilter::MaskedFFTNormalizedCorrelationImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MaskedFFTNormalizedCorrelationImageFilter::SetRequiredFractionOfOverlappingPixels "

Set and get the required fraction of overlapping pixels

";

%feature("docstring")  itk::simple::MaskedFFTNormalizedCorrelationImageFilter::SetRequiredNumberOfOverlappingPixels "

Set and get the required number of overlapping pixels

";

%feature("docstring")  itk::simple::MaskedFFTNormalizedCorrelationImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MaskedFFTNormalizedCorrelationImageFilter::~MaskedFFTNormalizedCorrelationImageFilter "

Destructor

";


%feature("docstring") itk::simple::MaximumEntropyThresholdImageFilter "

Threshold an image using the MaximumEntropy Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the MaximumEntropyThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::MaximumEntropyThreshold for the procedural interface

 itk::MaximumEntropyThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMaximumEntropyThresholdImageFilter.h
";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::MaximumEntropyThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MaximumEntropyThresholdImageFilter::~MaximumEntropyThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::MaximumImageFilter "

Implements a pixel-wise operator Max(a,b) between two images.


The pixel values of the output image are the maximum between the
corresponding pixels of the two input images.

This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.
See:
 itk::simple::Maximum for the procedural interface

 itk::MaximumImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMaximumImageFilter.h
";

%feature("docstring")  itk::simple::MaximumImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::MaximumImageFilter::Execute "
";

%feature("docstring")  itk::simple::MaximumImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::MaximumImageFilter::Execute "
";

%feature("docstring")  itk::simple::MaximumImageFilter::Execute "
";

%feature("docstring")  itk::simple::MaximumImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MaximumImageFilter::MaximumImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MaximumImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MaximumImageFilter::~MaximumImageFilter "

Destructor

";


%feature("docstring") itk::simple::MaximumProjectionImageFilter "

Maximum projection.


This class was contributed to the insight journal by Gaetan Lehmann.
The original paper can be found at https://hdl.handle.net/1926/164


Gaetan Lehmann. Biologie du Developpement et de la reproduction, inra
de jouy-en-josas, France.

See:
 ProjectionImageFilter

 MedianProjectionImageFilter

 MeanProjectionImageFilter

 MinimumProjectionImageFilter

 StandardDeviationProjectionImageFilter

 SumProjectionImageFilter

 BinaryProjectionImageFilter

 itk::simple::MaximumProjection for the procedural interface

 itk::MaximumProjectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMaximumProjectionImageFilter.h
";

%feature("docstring")  itk::simple::MaximumProjectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MaximumProjectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MaximumProjectionImageFilter::GetProjectionDimension "
";

%feature("docstring")  itk::simple::MaximumProjectionImageFilter::MaximumProjectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MaximumProjectionImageFilter::SetProjectionDimension "
";

%feature("docstring")  itk::simple::MaximumProjectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MaximumProjectionImageFilter::~MaximumProjectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::MeanImageFilter "

Applies an averaging filter to an image.


Computes an image where a given pixel is the mean value of the the
pixels in a neighborhood about the corresponding input pixel.

A mean filter is one of the family of linear filters.


See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::Mean for the procedural interface

 itk::MeanImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMeanImageFilter.h
";

%feature("docstring")  itk::simple::MeanImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MeanImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MeanImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::MeanImageFilter::MeanImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MeanImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::MeanImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::MeanImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MeanImageFilter::~MeanImageFilter "

Destructor

";


%feature("docstring") itk::simple::MeanProjectionImageFilter "

Mean projection.


This class was contributed to the Insight Journal by Gaetan Lehmann.
The original paper can be found at https://hdl.handle.net/1926/164


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ProjectionImageFilter

 MedianProjectionImageFilter

 MinimumProjectionImageFilter

 StandardDeviationProjectionImageFilter

 SumProjectionImageFilter

 BinaryProjectionImageFilter

 MaximumProjectionImageFilter

 itk::simple::MeanProjection for the procedural interface

 itk::MeanProjectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMeanProjectionImageFilter.h
";

%feature("docstring")  itk::simple::MeanProjectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MeanProjectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MeanProjectionImageFilter::GetProjectionDimension "
";

%feature("docstring")  itk::simple::MeanProjectionImageFilter::MeanProjectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MeanProjectionImageFilter::SetProjectionDimension "
";

%feature("docstring")  itk::simple::MeanProjectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MeanProjectionImageFilter::~MeanProjectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::MedianImageFilter "

Applies a median filter to an image.


Computes an image where a given pixel is the median value of the the
pixels in a neighborhood about the corresponding input pixel.

A median filter is one of the family of nonlinear filters. It is used
to smooth an image without being biased by outliers or shot noise.

This filter requires that the input pixel type provides an operator<() (LessThan Comparable).


See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::Median for the procedural interface

 itk::MedianImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMedianImageFilter.h
";

%feature("docstring")  itk::simple::MedianImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MedianImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MedianImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::MedianImageFilter::MedianImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MedianImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::MedianImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::MedianImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MedianImageFilter::~MedianImageFilter "

Destructor

";


%feature("docstring") itk::simple::MedianProjectionImageFilter "

Median projection.


This class was contributed to the Insight Journal by Gaetan Lehmann.
The original paper can be found at https://hdl.handle.net/1926/164


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ProjectionImageFilter

 StandardDeviationProjectionImageFilter

 SumProjectionImageFilter

 BinaryProjectionImageFilter

 MaximumProjectionImageFilter

 MinimumProjectionImageFilter

 MeanProjectionImageFilter

 itk::simple::MedianProjection for the procedural interface

 itk::MedianProjectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMedianProjectionImageFilter.h
";

%feature("docstring")  itk::simple::MedianProjectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MedianProjectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MedianProjectionImageFilter::GetProjectionDimension "
";

%feature("docstring")  itk::simple::MedianProjectionImageFilter::MedianProjectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MedianProjectionImageFilter::SetProjectionDimension "
";

%feature("docstring")  itk::simple::MedianProjectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MedianProjectionImageFilter::~MedianProjectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::MergeLabelMapFilter "

Merges several Label Maps.


This filter takes one or more input Label Map and merges them.

SetMethod() can be used to change how the filter manage the labels from the
different label maps. KEEP (0): MergeLabelMapFilter do its best to keep the label unchanged, but if a label is already
used in a previous label map, a new label is assigned. AGGREGATE (1):
If the same label is found several times in the label maps, the label
objects with the same label are merged. PACK (2): MergeLabelMapFilter relabel all the label objects by order of processing. No conflict can
occur. STRICT (3): MergeLabelMapFilter keeps the labels unchanged and raises an exception if the same label
is found in several images.

This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ShapeLabelObject , RelabelComponentImageFilter

 itk::simple::MergeLabelMapFilter for the procedural interface


C++ includes: sitkMergeLabelMapFilter.h
";

%feature("docstring")  itk::simple::MergeLabelMapFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::MergeLabelMapFilter::Execute "
";

%feature("docstring")  itk::simple::MergeLabelMapFilter::Execute "
";

%feature("docstring")  itk::simple::MergeLabelMapFilter::Execute "
";

%feature("docstring")  itk::simple::MergeLabelMapFilter::Execute "
";

%feature("docstring")  itk::simple::MergeLabelMapFilter::Execute "
";

%feature("docstring")  itk::simple::MergeLabelMapFilter::GetMethod "

Set/Get the method used to merge the label maps

";

%feature("docstring")  itk::simple::MergeLabelMapFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MergeLabelMapFilter::MergeLabelMapFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MergeLabelMapFilter::SetMethod "

Set/Get the method used to merge the label maps

";

%feature("docstring")  itk::simple::MergeLabelMapFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MergeLabelMapFilter::~MergeLabelMapFilter "

Destructor

";


%feature("docstring") itk::simple::MinMaxCurvatureFlowImageFilter "

Denoise an image using min/max curvature flow.


MinMaxCurvatureFlowImageFilter implements a curvature driven image denoising algorithm. Iso-
brightness contours in the grayscale input image are viewed as a level
set. The level set is then evolved using a curvature-based speed
function:

\\\\[ I_t = F_{\\\\mbox{minmax}} |\\\\nabla I| \\\\]

where $ F_{\\\\mbox{minmax}} = \\\\max(\\\\kappa,0) $ if $ \\\\mbox{Avg}_{\\\\mbox{stencil}}(x) $ is less than or equal to $ T_{threshold} $ and $ \\\\min(\\\\kappa,0) $ , otherwise. $ \\\\kappa $ is the mean curvature of the iso-brightness contour at point $ x $ .

In min/max curvature flow, movement is turned on or off depending on
the scale of the noise one wants to remove. Switching depends on the
average image value of a region of radius $ R $ around each point. The choice of $ R $ , the stencil radius, governs the scale of the noise to be removed.

The threshold value $ T_{threshold} $ is the average intensity obtained in the direction perpendicular to
the gradient at point $ x $ at the extrema of the local neighborhood.

This filter make use of the multi-threaded finite difference solver
hierarchy. Updates are computed using a MinMaxCurvatureFlowFunction object. A zero flux Neumann boundary condition is used when computing
derivatives near the data boundary.


WARNING:
This filter assumes that the input and output types have the same
dimensions. This filter also requires that the output image pixels are
of a real type. This filter works for any dimensional images, however
for dimensions greater than 3D, an expensive brute-force search is
used to compute the local threshold.
 Reference: \"Level Set Methods and Fast Marching Methods\", J.A.
Sethian, Cambridge Press, Chapter 16, Second edition, 1999.


See:
 MinMaxCurvatureFlowFunction

 CurvatureFlowImageFilter

 BinaryMinMaxCurvatureFlowImageFilter

 itk::simple::MinMaxCurvatureFlow for the procedural interface

 itk::MinMaxCurvatureFlowImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMinMaxCurvatureFlowImageFilter.h
";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::Execute "
";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::GetStencilRadius "

Set/Get the stencil radius.

";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::GetTimeStep "
";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::MinMaxCurvatureFlowImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::SetStencilRadius "

Set/Get the stencil radius.

";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::SetTimeStep "
";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MinMaxCurvatureFlowImageFilter::~MinMaxCurvatureFlowImageFilter "

Destructor

";


%feature("docstring") itk::simple::MinimumImageFilter "

Implements a pixel-wise operator Min(a,b) between two images.


The pixel values of the output image are the minimum between the
corresponding pixels of the two input images.

This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.
See:
 itk::simple::Minimum for the procedural interface

 itk::MinimumImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMinimumImageFilter.h
";

%feature("docstring")  itk::simple::MinimumImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::MinimumImageFilter::Execute "
";

%feature("docstring")  itk::simple::MinimumImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::MinimumImageFilter::Execute "
";

%feature("docstring")  itk::simple::MinimumImageFilter::Execute "
";

%feature("docstring")  itk::simple::MinimumImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MinimumImageFilter::MinimumImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MinimumImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MinimumImageFilter::~MinimumImageFilter "

Destructor

";


%feature("docstring") itk::simple::MinimumMaximumImageFilter "

Computes the minimum and the maximum intensity values of an image.


It is templated over input image type only.

This filter is automatically multi-threaded and can stream its input
when NumberOfStreamDivisions is set to more than
The extrema are independently computed for each streamed and threaded
region then merged.

See:
 StatisticsImageFilter

 itk::MinimumMaximumImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMinimumMaximumImageFilter.h
";

%feature("docstring")  itk::simple::MinimumMaximumImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MinimumMaximumImageFilter::GetMaximum "

Return the computed Maximum.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::MinimumMaximumImageFilter::GetMinimum "

Return the computed Minimum.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::MinimumMaximumImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MinimumMaximumImageFilter::MinimumMaximumImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MinimumMaximumImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MinimumMaximumImageFilter::~MinimumMaximumImageFilter "

Destructor

";


%feature("docstring") itk::simple::MinimumProjectionImageFilter "

Minimum projection.


This class was contributed to the Insight Journal by Gaetan Lehmann.
The original paper can be found at https://hdl.handle.net/1926/164


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ProjectionImageFilter

 StandardDeviationProjectionImageFilter

 SumProjectionImageFilter

 BinaryProjectionImageFilter

 MaximumProjectionImageFilter

 MeanProjectionImageFilter

 itk::simple::MinimumProjection for the procedural interface

 itk::MinimumProjectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMinimumProjectionImageFilter.h
";

%feature("docstring")  itk::simple::MinimumProjectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MinimumProjectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MinimumProjectionImageFilter::GetProjectionDimension "
";

%feature("docstring")  itk::simple::MinimumProjectionImageFilter::MinimumProjectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MinimumProjectionImageFilter::SetProjectionDimension "
";

%feature("docstring")  itk::simple::MinimumProjectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MinimumProjectionImageFilter::~MinimumProjectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::MirrorPadImageFilter "

Increase the image size by padding with replicants of the input image
value.


MirrorPadImageFilter changes the image bounds of an image. Any added pixels are filled in
with a mirrored replica of the input image. For instance, if the
output image needs a pixel that is two pixels to the left of the
LargestPossibleRegion of the input image, the value assigned will be
from the pixel two pixels inside the left boundary of the
LargestPossibleRegion. The image bounds of the output must be
specified.

Visual explanation of padding regions.

This filter is implemented as a multithreaded filter. It provides a
DynamicThreadedGenerateData() method for its implementation.

Exponential decay in the bounds is enabled when DecayBase has to be in
the range (0.0, 1.0]. When it is 1.0 it is disabled. The decay rate is
based on the Manhattan distance.


See:
 WrapPadImageFilter , ConstantPadImageFilter

 itk::simple::MirrorPad for the procedural interface

 itk::MirrorPadImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMirrorPadImageFilter.h
";

%feature("docstring")  itk::simple::MirrorPadImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MirrorPadImageFilter::GetDecayBase "

Get/Set the base for exponential decay in mirrored region.

";

%feature("docstring")  itk::simple::MirrorPadImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MirrorPadImageFilter::GetPadLowerBound "
";

%feature("docstring")  itk::simple::MirrorPadImageFilter::GetPadUpperBound "
";

%feature("docstring")  itk::simple::MirrorPadImageFilter::MirrorPadImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MirrorPadImageFilter::SetDecayBase "

Get/Set the base for exponential decay in mirrored region.

";

%feature("docstring")  itk::simple::MirrorPadImageFilter::SetPadLowerBound "
";

%feature("docstring")  itk::simple::MirrorPadImageFilter::SetPadUpperBound "
";

%feature("docstring")  itk::simple::MirrorPadImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MirrorPadImageFilter::~MirrorPadImageFilter "

Destructor

";


%feature("docstring") itk::simple::ModulusImageFilter "

Computes the modulus (x % dividend) pixel-wise.


The input pixel type must support the c++ modulus operator (%).

If the dividend is zero, the maximum value will be returned.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 itk::simple::Modulus for the procedural interface

 itk::ModulusImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkModulusImageFilter.h
";

%feature("docstring")  itk::simple::ModulusImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::ModulusImageFilter::Execute "
";

%feature("docstring")  itk::simple::ModulusImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::ModulusImageFilter::Execute "
";

%feature("docstring")  itk::simple::ModulusImageFilter::Execute "
";

%feature("docstring")  itk::simple::ModulusImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ModulusImageFilter::ModulusImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ModulusImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ModulusImageFilter::~ModulusImageFilter "

Destructor

";


%feature("docstring") itk::simple::MomentsThresholdImageFilter "

Threshold an image using the Moments Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the MomentsThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::MomentsThreshold for the procedural interface

 itk::MomentsThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMomentsThresholdImageFilter.h
";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::MomentsThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MomentsThresholdImageFilter::~MomentsThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::MorphologicalGradientImageFilter "

gray scale dilation of an image


Dilate an image using grayscale morphology. Dilation takes the maximum
of all the pixels identified by the structuring element.

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.


See:
 MorphologyImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter

 itk::simple::MorphologicalGradient for the procedural interface

 itk::MorphologicalGradientImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMorphologicalGradientImageFilter.h
";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::MorphologicalGradientImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MorphologicalGradientImageFilter::~MorphologicalGradientImageFilter "

Destructor

";


%feature("docstring") itk::simple::MorphologicalWatershedFromMarkersImageFilter "

Morphological watershed transform from markers.


The watershed transform is a tool for image segmentation that is fast
and flexible and potentially fairly parameter free. It was originally
derived from a geophysical model of rain falling on a terrain and a
variety of more formal definitions have been devised to allow
development of practical algorithms. If an image is considered as a
terrain and divided into catchment basins then the hope is that each
catchment basin would contain an object of interest.

The output is a label image. A label image, sometimes referred to as a
categorical image, has unique values for each region. For example, if
a watershed produces 2 regions, all pixels belonging to one region
would have value A, and all belonging to the other might have value B.
Unassigned pixels, such as watershed lines, might have the background
value (0 by convention).

The simplest way of using the watershed is to preprocess the image we
want to segment so that the boundaries of our objects are bright (e.g
apply an edge detector) and compute the watershed transform of the
edge image. Watershed lines will correspond to the boundaries and our
problem will be solved. This is rarely useful in practice because
there are always more regional minima than there are objects, either
due to noise or natural variations in the object surfaces. Therefore,
while many watershed lines do lie on significant boundaries, there are
many that don't. Various methods can be used to reduce the number of
minima in the image, like thresholding the smallest values, filtering
the minima and/or smoothing the image.

This filter use another approach to avoid the problem of over
segmentation: it let the user provide a marker image which mark the
minima in the input image and give them a label. The minima are
imposed in the input image by the markers. The labels of the output
image are the label of the marker image.

The morphological watershed transform algorithm is described in
Chapter 9.2 of Pierre Soille's book \"Morphological Image Analysis:
Principles and Applications\", Second Edition, Springer, 2003.

This code was contributed in the Insight Journal paper: \"The
watershed transform in ITK - discussion and new developments\" by
Beare R., Lehmann G. https://hdl.handle.net/1926/202 http://www.insight-journal.org/browse/publication/92


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

See:
 WatershedImageFilter , MorphologicalWatershedImageFilter

 itk::simple::MorphologicalWatershedFromMarkers for the procedural interface

 itk::MorphologicalWatershedFromMarkersImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMorphologicalWatershedFromMarkersImageFilter.h
";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::GetMarkWatershedLine "

Set/Get whether the watershed pixel must be marked or not. Default is
true. Set it to false do not only avoid writing watershed pixels, it
also decrease algorithm complexity.

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::MarkWatershedLineOff "
";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::MarkWatershedLineOn "

Set the value of MarkWatershedLine to true or false respectfully.

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::MorphologicalWatershedFromMarkersImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::SetMarkWatershedLine "

Set/Get whether the watershed pixel must be marked or not. Default is
true. Set it to false do not only avoid writing watershed pixels, it
also decrease algorithm complexity.

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MorphologicalWatershedFromMarkersImageFilter::~MorphologicalWatershedFromMarkersImageFilter "

Destructor

";


%feature("docstring") itk::simple::MorphologicalWatershedImageFilter "

Watershed segmentation implementation with morphological operators.


Watershed pixel are labeled 0. TOutputImage should be an integer type.
Labels of output image are in no particular order. You can reorder the
labels such that object labels are consecutive and sorted based on
object size by passing the output of this filter to a RelabelComponentImageFilter .

The morphological watershed transform algorithm is described in
Chapter 9.2 of Pierre Soille's book \"Morphological Image Analysis:
Principles and Applications\", Second Edition, Springer, 2003.

This code was contributed in the Insight Journal paper: \"The
watershed transform in ITK - discussion and new developments\" by
Beare R., Lehmann G. https://hdl.handle.net/1926/202 http://www.insight-journal.org/browse/publication/92


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 WatershedImageFilter , MorphologicalWatershedFromMarkersImageFilter

 itk::simple::MorphologicalWatershed for the procedural interface

 itk::MorphologicalWatershedImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMorphologicalWatershedImageFilter.h
";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::GetLevel "
";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::GetMarkWatershedLine "

Set/Get whether the watershed pixel must be marked or not. Default is
true. Set it to false do not only avoid writing watershed pixels, it
also decrease algorithm complexity.

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::MarkWatershedLineOff "
";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::MarkWatershedLineOn "

Set the value of MarkWatershedLine to true or false respectfully.

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::MorphologicalWatershedImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::SetLevel "
";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::SetMarkWatershedLine "

Set/Get whether the watershed pixel must be marked or not. Default is
true. Set it to false do not only avoid writing watershed pixels, it
also decrease algorithm complexity.

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MorphologicalWatershedImageFilter::~MorphologicalWatershedImageFilter "

Destructor

";


%feature("docstring") itk::simple::MultiLabelSTAPLEImageFilter "

This filter performs a pixelwise combination of an arbitrary number of
input images, where each of them represents a segmentation of the same
scene (i.e., image).


The labelings in the images are weighted relative to each other based
on their \"performance\" as estimated by an expectation-maximization
algorithm. In the process, a ground truth segmentation is estimated,
and the estimated performances of the individual segmentations are
relative to this estimated ground truth.

The algorithm is based on the binary STAPLE algorithm by Warfield et
al. as published originally in

S. Warfield, K. Zou, W. Wells, \"Validation of image segmentation and
expert  quality with an expectation-maximization algorithm\" in MICCAI
2002: Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag,
Heidelberg, Germany, 2002, pp. 298-306

The multi-label algorithm implemented here is described in detail in

T. Rohlfing, D. B. Russakoff, and C. R. Maurer, Jr., \"Performance-
based classifier combination in atlas-based image segmentation using
expectation-maximization parameter estimation,\" IEEE Transactions on
Medical Imaging, vol. 23, pp. 983-994, Aug. 2004.

INPUTS
All input volumes to this filter must be segmentations of an image,
that is, they must have discrete pixel values where each value
represents a different segmented object.
 Input volumes must all contain the same size RequestedRegions. Not all input images must contain all possible labels, but all label
values must have the same meaning in all images.

The filter can optionally be provided with estimates for the a priori
class probabilities through the SetPriorProbabilities function. If no
estimate is provided, one is automatically generated by analyzing the
relative frequencies of the labels in the input images.

OUTPUTS
The filter produces a single output volume. Each output pixel contains
the label that has the highest probability of being the correct label,
based on the performance models of the individual segmentations. If
the maximum probability is not unique, i.e., if more than one label
have a maximum probability, then an \"undecided\" label is assigned to
that output pixel.
 By default, the label used for undecided pixels is the maximum label
value used in the input images plus one. Since it is possible for an
image with 8 bit pixel values to use all 256 possible label values, it
is permissible to combine 8 bit (i.e., byte) images into a 16 bit
(i.e., short) output image.

In addition to the combined image, the estimated confusion matrices
for each of the input segmentations can be obtained through the
GetConfusionMatrix member function.

PARAMETERS
The label used for \"undecided\" labels can be set using
SetLabelForUndecidedPixels. This functionality can be unset by calling
UnsetLabelForUndecidedPixels.
 A termination threshold for the EM iteration can be defined by
calling SetTerminationUpdateThreshold. The iteration terminates once
no single parameter of any confusion matrix changes by less than this
threshold. Alternatively, a maximum number of iterations can be
specified by calling SetMaximumNumberOfIterations. The algorithm may
still terminate after a smaller number of iterations if the
termination threshold criterion is satisfied.

EVENTS
This filter invokes IterationEvent() at each iteration of the E-M
algorithm. Setting the AbortGenerateData() flag will cause the
algorithm to halt after the current iteration and produce results just
as if it had converged. The algorithm makes no attempt to report its
progress since the number of iterations needed cannot be known in
advance.

Torsten Rohlfing, SRI International, Neuroscience Program

See:
 itk::simple::MultiLabelSTAPLE for the procedural interface


C++ includes: sitkMultiLabelSTAPLEImageFilter.h
";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::GetConfusionMatrix "

Get confusion matrix for the i-th input segmentation.

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::GetLabelForUndecidedPixels "

Get label value used for undecided pixels.

After updating the filter, this function returns the actual label
value used for undecided pixels in the current output. Note that this
value is overwritten when SetLabelForUndecidedPixels is called and the
new value only becomes effective upon the next filter update.

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::GetMaximumNumberOfIterations "

Set maximum number of iterations.

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::GetPriorProbabilities "

Get prior class probabilities.

After updating the filter, this function returns the actual prior
class probabilities. If these were not previously set by a call to
SetPriorProbabilities, then they are estimated from the input
segmentations and the result is available through this function.

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::GetTerminationUpdateThreshold "

Set termination threshold based on confusion matrix parameter updates.

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::MultiLabelSTAPLEImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::SetLabelForUndecidedPixels "

Set label value for undecided pixels.

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::SetMaximumNumberOfIterations "

Set maximum number of iterations.

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::SetPriorProbabilities "

Set manual estimates for the a priori class probabilities. The size of
the array must be greater than the value of the largest label. The
index into the array corresponds to the label value in the segmented
image for the class.

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::SetTerminationUpdateThreshold "

Set termination threshold based on confusion matrix parameter updates.

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MultiLabelSTAPLEImageFilter::~MultiLabelSTAPLEImageFilter "

Destructor

";


%feature("docstring") itk::simple::MultiplyImageFilter "

Pixel-wise multiplication of two images.


This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.
See:
 itk::simple::Multiply for the procedural interface

 itk::MultiplyImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkMultiplyImageFilter.h
";

%feature("docstring")  itk::simple::MultiplyImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::MultiplyImageFilter::Execute "
";

%feature("docstring")  itk::simple::MultiplyImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::MultiplyImageFilter::Execute "
";

%feature("docstring")  itk::simple::MultiplyImageFilter::Execute "
";

%feature("docstring")  itk::simple::MultiplyImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::MultiplyImageFilter::MultiplyImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::MultiplyImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::MultiplyImageFilter::~MultiplyImageFilter "

Destructor

";


%feature("docstring") itk::simple::N4BiasFieldCorrectionImageFilter "

Implementation of the N4 bias field correction algorithm.


The nonparametric nonuniform intensity normalization (N3) algorithm,
as introduced by Sled et al. in 1998 is a method for correcting
nonuniformity associated with MR images. The algorithm assumes a
simple parametric model (Gaussian) for the bias field and does not
require tissue class segmentation. In addition, there are only a
couple of parameters to tune with the default values performing quite
well. N3 has been publicly available as a set of perl scripts ( http://www.bic.mni.mcgill.ca/ServicesSoftwareAdvancedImageProcessingTo
ols/HomePage )

The N4 algorithm, encapsulated with this class, is a variation of the
original N3 algorithm with the additional benefits of an improved
B-spline fitting routine which allows for multiple resolutions to be
used during the correction process. We also modify the iterative
update component of algorithm such that the residual bias field is
continually updated

Notes for the user:
Since much of the image manipulation is done in the log space of the
intensities, input images with negative and small values (< 1) can
produce poor results.

The original authors recommend performing the bias field correction on
a downsampled version of the original image.

A binary mask or a weighted image can be supplied. If a binary mask is
specified, those voxels in the input image which correspond to the
voxels in the mask image are used to estimate the bias field. If a
UseMaskLabel value is set to false (the default), all non-zero voxels
in the MaskImage will be masked; otherwise only voxels in the
MaskImage that match the MaskLabel will be used. If a confidence image
is specified, the input voxels are weighted in the b-spline fitting
routine according to the confidence voxel values.

The filter returns the corrected image. If the bias field is wanted,
one can reconstruct it using the class
itkBSplineControlPointImageFilter. See the IJ article and the test
file for an example.

The 'Z' parameter in Sled's 1998 paper is the square root of the class
variable 'm_WienerFilterNoise'.
 The basic algorithm iterates between sharpening the intensity
histogram of the corrected input image and spatially smoothing those
results with a B-spline scalar field estimate of the bias field.


Nicholas J. Tustison
 Contributed by Nicholas J. Tustison, James C. Gee in the Insight
Journal paper: https://hdl.handle.net/10380/3053

REFERENCE
 J.G. Sled, A.P. Zijdenbos and A.C. Evans. \"A Nonparametric Method
for Automatic Correction of Intensity Nonuniformity in Data\" IEEE
Transactions on Medical Imaging, Vol 17, No 1. Feb 1998.

N.J. Tustison, B.B. Avants, P.A. Cook, Y. Zheng, A. Egan, P.A.
Yushkevich, and J.C. Gee. \"N4ITK: Improved N3 Bias Correction\" IEEE
Transactions on Medical Imaging, 29(6):1310-1320, June 2010.
See:
 itk::simple::N4BiasFieldCorrection for the procedural interface

 itk::N4BiasFieldCorrectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkN4BiasFieldCorrectionImageFilter.h
";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::Execute "
";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetBiasFieldFullWidthAtHalfMaximum "

Get the full width at half maximum parameter characterizing the width
of the Gaussian deconvolution. Default = 0.15.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetConvergenceThreshold "

Get the convergence threshold. Convergence is determined by the
coefficient of variation of the difference image between the current
bias field estimate and the previous estimate. If this value is less
than the specified threshold, the algorithm proceeds to the next
fitting level or terminates if it is at the last level.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetMaskLabel "

DeprecatedSet/Get mask label value. If a binary mask image is
specified and if UseMaskValue is true, only those input image voxels
corresponding with mask image values equal to MaskLabel are used in
estimating the bias field. If a MaskImage is specified and
UseMaskLabel is false, all input image voxels corresponding to non-
zero voxels in the MaskImage are used in estimating the bias field.
Default = 1.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetMaximumNumberOfIterations "

Get the maximum number of iterations specified at each fitting level.
Default = 50.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetNumberOfControlPoints "

Get the control point grid size defining the B-spline estimate of the
scalar bias field. In each dimension, the B-spline mesh size is equal
to the number of control points in that dimension minus the spline
order. Default = 4 control points in each dimension for a mesh size of
1 in each dimension.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetNumberOfHistogramBins "

Get number of bins defining the log input intensity histogram. Default
= 200.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetSplineOrder "

Get the spline order defining the bias field estimate. Default = 3.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetUseMaskLabel "

Use a mask label for identifying mask functionality. See SetMaskLabel.
Defaults to true.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::GetWienerFilterNoise "

Get the noise estimate defining the Wiener filter. Default = 0.01.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::N4BiasFieldCorrectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetBiasFieldFullWidthAtHalfMaximum "

Set the full width at half maximum parameter characterizing the width
of the Gaussian deconvolution. Default = 0.15.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetConvergenceThreshold "

Set the convergence threshold. Convergence is determined by the
coefficient of variation of the difference image between the current
bias field estimate and the previous estimate. If this value is less
than the specified threshold, the algorithm proceeds to the next
fitting level or terminates if it is at the last level.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetMaskLabel "

Set/Get mask label value. If a binary mask image is specified and if
UseMaskValue is true, only those input image voxels corresponding with
mask image values equal to MaskLabel are used in estimating the bias
field. If a MaskImage is specified and UseMaskLabel is false, all
input image voxels corresponding to non-zero voxels in the MaskImage
are used in estimating the bias field. Default = 1.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetMaximumNumberOfIterations "

Set the maximum number of iterations specified at each fitting level.
Default = 50.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetNumberOfControlPoints "

Set the control point grid size defining the B-spline estimate of the
scalar bias field. In each dimension, the B-spline mesh size is equal
to the number of control points in that dimension minus the spline
order. Default = 4 control points in each dimension for a mesh size of
1 in each dimension.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetNumberOfControlPoints "

Set the values of the NumberOfControlPoints vector all to value

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetNumberOfHistogramBins "

Set number of bins defining the log input intensity histogram. Default
= 200.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetSplineOrder "

Set the spline order defining the bias field estimate. Default = 3.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetUseMaskLabel "

Use a mask label for identifying mask functionality. See SetMaskLabel.
Defaults to true.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::SetWienerFilterNoise "

Set the noise estimate defining the Wiener filter. Default = 0.01.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::UseMaskLabelOff "
";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::UseMaskLabelOn "

Set the value of UseMaskLabel to true or false respectfully.

";

%feature("docstring")  itk::simple::N4BiasFieldCorrectionImageFilter::~N4BiasFieldCorrectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::NaryAddImageFilter "

Pixel-wise addition of N images.


This class is templated over the types of the input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.

The pixel type of the input images must have a valid definition of the
operator+ with each other. This condition is required because
internally this filter will perform the operation


Additionally the type resulting from the sum, will be cast to the
pixel type of the output image.

The total operation over one pixel will be


For example, this filter could be used directly for adding images
whose pixels are vectors of the same dimension, and to store the
resulting vector in an output image of vector pixels.


WARNING:
No numeric overflow checking is performed in this filter.

See:
 itk::simple::NaryAdd for the procedural interface


C++ includes: sitkNaryAddImageFilter.h
";

%feature("docstring")  itk::simple::NaryAddImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::NaryAddImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryAddImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryAddImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryAddImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryAddImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryAddImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::NaryAddImageFilter::NaryAddImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::NaryAddImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::NaryAddImageFilter::~NaryAddImageFilter "

Destructor

";


%feature("docstring") itk::simple::NaryMaximumImageFilter "

Computes the pixel-wise maximum of several images.


This class is templated over the types of the input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.

The pixel type of the output images must have a valid definition of
the operator<. This condition is required because internally this
filter will perform an operation similar to:

 (where current_maximum is also of type OutputPixelType)

for each of the n input images.

For example, this filter could be used directly to find a \"maximum
projection\" of a series of images, often used in preliminary analysis
of time-series data.


Zachary Pincus
 This filter was contributed by Zachary Pincus from the Department of
Biochemistry and Program in Biomedical Informatics at Stanford
University School of Medicine


See:
 itk::simple::NaryMaximum for the procedural interface


C++ includes: sitkNaryMaximumImageFilter.h
";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::Execute "
";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::NaryMaximumImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::NaryMaximumImageFilter::~NaryMaximumImageFilter "

Destructor

";


%feature("docstring") itk::simple::NeighborhoodConnectedImageFilter "

Label pixels that are connected to a seed and lie within a neighborhood.


NeighborhoodConnectedImageFilter labels pixels with ReplaceValue that are connected to an initial Seed
AND whose neighbors all lie within a Lower and Upper threshold range.
See:
 itk::simple::NeighborhoodConnected for the procedural interface

 itk::NeighborhoodConnectedImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkNeighborhoodConnectedImageFilter.h
";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::AddSeed "

Add SeedList point.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::ClearSeeds "

Remove all SeedList points.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::GetLower "

Set/Get the lower threshold. The default is 0.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::GetRadius "

Get the radius of the neighborhood used to compute the median

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::GetReplaceValue "

Set/Get value to replace thresholded pixels. Pixels that lie * within
Lower and Upper (inclusive) will be replaced with this value. The
default is 1.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::GetSeedList "

Get list of seeds.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::GetUpper "

Set/Get the upper threshold. The default is the largest possible value
for the InputPixelType.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::NeighborhoodConnectedImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::SetLower "

Set/Get the lower threshold. The default is 0.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::SetRadius "

Set the radius of the neighborhood used for a mask.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::SetReplaceValue "

Set/Get value to replace thresholded pixels. Pixels that lie * within
Lower and Upper (inclusive) will be replaced with this value. The
default is 1.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::SetSeedList "

Set list of image indexes for seeds.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::SetUpper "

Set/Get the upper threshold. The default is the largest possible value
for the InputPixelType.

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::NeighborhoodConnectedImageFilter::~NeighborhoodConnectedImageFilter "

Destructor

";


%feature("docstring") itk::simple::NoiseImageFilter "

Calculate the local noise in an image.


Computes an image where a given pixel is the standard deviation of the
pixels in a neighborhood about the corresponding input pixel. This
serves as an estimate of the local noise (or texture) in an image.
Currently, this noise estimate assume a piecewise constant image. This
filter should be extended to fitting a (hyper) plane to the
neighborhood and calculating the standard deviation of the residuals
to this (hyper) plane.


See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::Noise for the procedural interface

 itk::NoiseImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkNoiseImageFilter.h
";

%feature("docstring")  itk::simple::NoiseImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::NoiseImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::NoiseImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::NoiseImageFilter::NoiseImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::NoiseImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::NoiseImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::NoiseImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::NoiseImageFilter::~NoiseImageFilter "

Destructor

";


%feature("docstring") itk::simple::NonCopyable "

An inheritable class to disable copying of a class.


This class disable the implicit implementations of the assignment and
copy constructor for derived classes. The instantiation of the default
implementation for either method in a derived class will result in a
compile-time error because they are private in this class. However,
this policy is not absolute for derived classes because explicit
implementation of these methods could be implemented.

An advantage this approach has is the class hierarchy makes it obvious
what the intent is, as compared to other approaches.

For example you should not be able to copy singleton object, because
there should only be one of them. To utilize this class just derive
from it:

C++ includes: sitkNonCopyable.h
";

%feature("docstring")  itk::simple::NonCopyable::NonCopyable "
";


%feature("docstring") itk::simple::NormalizeImageFilter "

Normalize an image by setting its mean to zero and variance to one.


NormalizeImageFilter shifts and scales an image so that the pixels in the image have a
zero mean and unit variance. This filter uses StatisticsImageFilter to compute the mean and variance of the input and then applies ShiftScaleImageFilter to shift and scale the pixels.

NB: since this filter normalizes the data such that the mean is at 0,
and $-\\\\sigma$ to $+\\\\sigma$ is mapped to -1.0 to 1.0, output image integral types will produce an
image that DOES NOT HAVE a unit variance due to 68% of the intensity
values being mapped to the real number range of -1.0 to 1.0 and then
cast to the output integral value.


See:
 NormalizeToConstantImageFilter

 itk::simple::Normalize for the procedural interface

 itk::NormalizeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkNormalizeImageFilter.h
";

%feature("docstring")  itk::simple::NormalizeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::NormalizeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::NormalizeImageFilter::NormalizeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::NormalizeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::NormalizeImageFilter::~NormalizeImageFilter "

Destructor

";


%feature("docstring") itk::simple::NormalizeToConstantImageFilter "

Scales image pixel intensities to make the sum of all pixels equal a
user-defined constant.


The default value of the constant is 1. It can be changed with SetConstant() .

This transform is especially useful for normalizing a convolution
kernel.

This code was contributed in the Insight Journal paper: \"FFT based
convolution\" by Lehmann G. https://hdl.handle.net/10380/3154


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 NormalizeImageFilter

 StatisticsImageFilter

 DivideImageFilter

 itk::simple::NormalizeToConstant for the procedural interface

 itk::NormalizeToConstantImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkNormalizeToConstantImageFilter.h
";

%feature("docstring")  itk::simple::NormalizeToConstantImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::NormalizeToConstantImageFilter::GetConstant "

Set/get the normalization constant.

";

%feature("docstring")  itk::simple::NormalizeToConstantImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::NormalizeToConstantImageFilter::NormalizeToConstantImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::NormalizeToConstantImageFilter::SetConstant "

Set/get the normalization constant.

";

%feature("docstring")  itk::simple::NormalizeToConstantImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::NormalizeToConstantImageFilter::~NormalizeToConstantImageFilter "

Destructor

";


%feature("docstring") itk::simple::NormalizedCorrelationImageFilter "

Computes the normalized correlation of an image and a template.


This filter calculates the normalized correlation between an image and
the template. Normalized correlation is frequently use in feature
detection because it is invariant to local changes in contrast.

The filter can be given a mask. When presented with an input image and
a mask, the normalized correlation is only calculated at those pixels
under the mask.


See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::NormalizedCorrelation for the procedural interface

 itk::NormalizedCorrelationImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkNormalizedCorrelationImageFilter.h
";

%feature("docstring")  itk::simple::NormalizedCorrelationImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::NormalizedCorrelationImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::NormalizedCorrelationImageFilter::NormalizedCorrelationImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::NormalizedCorrelationImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::NormalizedCorrelationImageFilter::~NormalizedCorrelationImageFilter "

Destructor

";


%feature("docstring") itk::simple::NotEqualImageFilter "

Implements pixel-wise generic operation of two images, or of an image
and a constant.


This class is parameterized over the types of the two input images and
the type of the output image. It is also parameterized by the
operation to be applied. A Functor style is used.

The constant must be of the same type than the pixel type of the
corresponding image. It is wrapped in a SimpleDataObjectDecorator so it can be updated through the pipeline. The SetConstant() and
GetConstant() methods are provided as shortcuts to set or get the
constant value without manipulating the decorator.


See:
 BinaryGeneratorImagFilter

 UnaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::NotEqual for the procedural interface

 itk::BinaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkNotEqualImageFilter.h
";

%feature("docstring")  itk::simple::NotEqualImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::NotEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::NotEqualImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::NotEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::NotEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::NotEqualImageFilter::Execute "

Execute the filter on an image and a constant with the given
parameters

";

%feature("docstring")  itk::simple::NotEqualImageFilter::Execute "
";

%feature("docstring")  itk::simple::NotEqualImageFilter::GetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::NotEqualImageFilter::GetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::NotEqualImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::NotEqualImageFilter::NotEqualImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::NotEqualImageFilter::SetBackgroundValue "

Set/Get the value used to mark the false pixels of the operator.

";

%feature("docstring")  itk::simple::NotEqualImageFilter::SetForegroundValue "

Set/Get the value used to mark the true pixels of the operator.

";

%feature("docstring")  itk::simple::NotEqualImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::NotEqualImageFilter::~NotEqualImageFilter "

Destructor

";


%feature("docstring") itk::simple::NotImageFilter "

Implements the NOT logical operator pixel-wise on an image.


This class is templated over the type of an input image and the type
of the output image. Numeric conversions (castings) are done by the
C++ defaults.

Since the logical NOT operation operates only on boolean types, the
input type must be implicitly convertible to bool, which is only
defined in C++ for integer types, the images passed to this filter
must comply with the requirement of using integer pixel type.

The total operation over one pixel will be


Where \"!\" is the unary Logical NOT operator in C++.
See:
 itk::simple::Not for the procedural interface

 itk::NotImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkNotImageFilter.h
";

%feature("docstring")  itk::simple::NotImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::NotImageFilter::Execute "
";

%feature("docstring")  itk::simple::NotImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::NotImageFilter::NotImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::NotImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::NotImageFilter::~NotImageFilter "

Destructor

";


%feature("docstring") itk::simple::ObjectnessMeasureImageFilter "

Enhance M-dimensional objects in N-dimensional images.


This filter is a generalization of Frangi's vesselness measurement for
detecting M-dimensional object in N-dimensional space. For example a
vessel is a 1-D object in 3-D space. The filter can enhance blob-like
structures (M=0), vessel-like structures (M=1), 2D plate-like
structures (M=2), hyper-plate-like structures (M=3) in N-dimensional
images, with M<N.

This filter takes a scalar image as input and produces a real valued
image as output which contains the objectness measure at each pixel.
Internally, it computes a Hessian via discrete central differences.
Before applying this filter it is expected that a Gaussian smoothing
filter at an appropriate scale (sigma) was applied to the input image.

The enhancement is based on the eigenvalues of the Hessian matrix. For
the Frangi's vesselness case were M=1 and N=3 we have the 3
eigenvalues such that $ | \\\\lambda_1 | < | \\\\lambda_2 | < |\\\\lambda_3 | $ . The formula follows:

\\\\[ R_A = \\\\frac{|\\\\lambda_2|}{|\\\\lambda_3|}, \\\\; R_B =
\\\\frac{|\\\\lambda_2|}{|\\\\lambda_2\\\\lambda_3|}, \\\\; S =
\\\\sqrt{\\\\lambda_1^2+\\\\lambda_2^2+\\\\lambda_3^2} \\\\] \\\\[ V_{\\\\sigma}= \\\\begin{cases}
(1-e^{-\\\\frac{R_A^2}{2\\\\alpha^2}}) \\\\cdot
e^{\\\\frac{R_B^2}{2\\\\beta^2}} \\\\cdot
(1-e^{-\\\\frac{S^2}{2\\\\gamma^2}}) & \\\\text{if } \\\\lambda_2<0
\\\\text{ and } \\\\lambda_3<0 \\\\text{,}\\\\\\\\ 0 &
\\\\text{otherwise} \\\\end{cases} \\\\]

References
Antiga, L. Generalizing vesselness with respect to dimensionality and
shape. https://hdl.handle.net/1926/576

Frangi, AF, Niessen, WJ, Vincken, KL, & Viergever, MA (1998).
Multiscale Vessel Enhancement Filtering. In Wells, WM, Colchester, A,
& Delp, S, Editors, MICCAI '98 Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in
Computer Science, pages 130-137, Springer Verlag, 1998.

See:
 itk::HessianToObjectnessMeasureImageFilter

 itk::simple::ObjectnessMeasure for the procedural interface

 itk::ObjectnessMeasureImageFilter for the Doxygen on the original ITK
class.


C++ includes: sitkObjectnessMeasureImageFilter.h
";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::BrightObjectOff "
";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::BrightObjectOn "

Set the value of BrightObject to true or false respectfully.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::GetAlpha "

Set/Get Alpha, the weight corresponding to R_A (the ratio of the
smallest eigenvalue that has to be large to the larger ones). Smaller
values lead to increased sensitivity to the object dimensionality.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::GetBeta "

Set/Get Beta, the weight corresponding to R_B (the ratio of the
largest eigenvalue that has to be small to the larger ones). Smaller
values lead to increased sensitivity to the object dimensionality.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::GetBrightObject "

Enhance bright structures on a dark background if true, the opposite
if false.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::GetGamma "

Set/Get Gamma, the weight corresponding to S (the Frobenius norm of
the Hessian matrix, or second-order structureness)

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::GetObjectDimension "

Set/Get the dimensionality of the object (0: points (blobs), 1: lines
(vessels), 2: planes (plate-like structures), 3: hyper-planes.
ObjectDimension must be smaller than ImageDimension.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::GetScaleObjectnessMeasure "

Toggle scaling the objectness measure with the magnitude of the
largest absolute eigenvalue

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::ObjectnessMeasureImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::ScaleObjectnessMeasureOff "
";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::ScaleObjectnessMeasureOn "

Set the value of ScaleObjectnessMeasure to true or false respectfully.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::SetAlpha "

Set/Get Alpha, the weight corresponding to R_A (the ratio of the
smallest eigenvalue that has to be large to the larger ones). Smaller
values lead to increased sensitivity to the object dimensionality.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::SetBeta "

Set/Get Beta, the weight corresponding to R_B (the ratio of the
largest eigenvalue that has to be small to the larger ones). Smaller
values lead to increased sensitivity to the object dimensionality.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::SetBrightObject "

Enhance bright structures on a dark background if true, the opposite
if false.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::SetGamma "

Set/Get Gamma, the weight corresponding to S (the Frobenius norm of
the Hessian matrix, or second-order structureness)

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::SetObjectDimension "

Set/Get the dimensionality of the object (0: points (blobs), 1: lines
(vessels), 2: planes (plate-like structures), 3: hyper-planes.
ObjectDimension must be smaller than ImageDimension.

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::SetScaleObjectnessMeasure "

Toggle scaling the objectness measure with the magnitude of the
largest absolute eigenvalue

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ObjectnessMeasureImageFilter::~ObjectnessMeasureImageFilter "

Destructor

";


%feature("docstring") itk::simple::OpeningByReconstructionImageFilter "

Opening by reconstruction of an image.


This filter preserves regions, in the foreground, that can completely
contain the structuring element. At the same time, this filter
eliminates all other regions of foreground pixels. Contrary to the
morphological opening, the opening by reconstruction preserves the
shape of the components that are not removed by erosion. The opening
by reconstruction of an image \"f\" is defined as:

OpeningByReconstruction(f) = DilationByRecontruction(f, Erosion(f)).

Opening by reconstruction not only removes structures destroyed by the
erosion, but also levels down the contrast of the brightest regions.
If PreserveIntensities is on, a subsequent reconstruction by dilation
using a marker image that is the original image for all unaffected
pixels.

Opening by reconstruction is described in Chapter 6.3.9 of Pierre
Soille's book \"Morphological Image Analysis: Principles and
Applications\", Second Edition, Springer, 2003.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 GrayscaleMorphologicalOpeningImageFilter

 itk::simple::OpeningByReconstruction for the procedural interface

 itk::OpeningByReconstructionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkOpeningByReconstructionImageFilter.h
";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::GetPreserveIntensities "

Set/Get whether the original intensities of the image retained for
those pixels unaffected by the opening by reconstrcution. If Off, the
output pixel contrast will be reduced.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::OpeningByReconstructionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::PreserveIntensitiesOff "
";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::PreserveIntensitiesOn "

Set the value of PreserveIntensities to true or false respectfully.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::SetPreserveIntensities "

Set/Get whether the original intensities of the image retained for
those pixels unaffected by the opening by reconstrcution. If Off, the
output pixel contrast will be reduced.

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::OpeningByReconstructionImageFilter::~OpeningByReconstructionImageFilter "

Destructor

";


%feature("docstring") itk::simple::OrImageFilter "

Implements the OR bitwise operator pixel-wise between two images.


This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.

Since the bitwise OR operation is only defined in C++ for integer
types, the images passed to this filter must comply with the
requirement of using integer pixel type.

The total operation over one pixel will be


Where \"|\" is the boolean OR operator in C++.
See:
 itk::simple::Or for the procedural interface

 itk::OrImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkOrImageFilter.h
";

%feature("docstring")  itk::simple::OrImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::OrImageFilter::Execute "
";

%feature("docstring")  itk::simple::OrImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::OrImageFilter::Execute "
";

%feature("docstring")  itk::simple::OrImageFilter::Execute "
";

%feature("docstring")  itk::simple::OrImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::OrImageFilter::OrImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::OrImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::OrImageFilter::~OrImageFilter "

Destructor

";


%feature("docstring") itk::simple::OtsuMultipleThresholdsImageFilter "

Threshold an image using multiple Otsu Thresholds.


This filter creates a labeled image that separates the input image
into various classes. The filter computes the thresholds using the OtsuMultipleThresholdsCalculator and applies those thresholds to the input image using the ThresholdLabelerImageFilter . The NumberOfHistogramBins and NumberOfThresholds can be set for the
Calculator. The LabelOffset can be set for the ThresholdLabelerImageFilter .

This filter also includes an option to use the valley emphasis
algorithm from H.F. Ng, \"Automatic thresholding for defect
detection\", Pattern Recognition Letters, (27): 1644-1649, 2006. The
valley emphasis algorithm is particularly effective when the object to
be thresholded is small. See the following tests for examples:
itkOtsuMultipleThresholdsImageFilterTest3 and
itkOtsuMultipleThresholdsImageFilterTest4 To use this algorithm,
simple call the setter: SetValleyEmphasis(true) It is turned off by
default.


See:
 ScalarImageToHistogramGenerator

 OtsuMultipleThresholdsCalculator

 ThresholdLabelerImageFilter

 itk::simple::OtsuMultipleThresholds for the procedural interface

 itk::OtsuMultipleThresholdsImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkOtsuMultipleThresholdsImageFilter.h
";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::GetLabelOffset "

Set/Get the offset which labels have to start from. Default is 0.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::GetNumberOfHistogramBins "

Set/Get the number of histogram bins. Default is 128.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::GetNumberOfThresholds "

Set/Get the number of thresholds. Default is 1.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::GetReturnBinMidpoint "
";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::GetThresholds "

Get the computed threshold.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::GetValleyEmphasis "

Set/Get the use of valley emphasis. Default is false.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::OtsuMultipleThresholdsImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::ReturnBinMidpointOff "
";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::ReturnBinMidpointOn "

Set the value of ReturnBinMidpoint to true or false respectfully.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::SetLabelOffset "

Set/Get the offset which labels have to start from. Default is 0.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins. Default is 128.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::SetNumberOfThresholds "

Set/Get the number of thresholds. Default is 1.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::SetReturnBinMidpoint "
";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::SetValleyEmphasis "

Set/Get the use of valley emphasis. Default is false.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::ValleyEmphasisOff "
";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::ValleyEmphasisOn "

Set the value of ValleyEmphasis to true or false respectfully.

";

%feature("docstring")  itk::simple::OtsuMultipleThresholdsImageFilter::~OtsuMultipleThresholdsImageFilter "

Destructor

";


%feature("docstring") itk::simple::OtsuThresholdImageFilter "

Threshold an image using the Otsu Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the OtsuThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::OtsuThreshold for the procedural interface

 itk::OtsuThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkOtsuThresholdImageFilter.h
";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::GetReturnBinMidpoint "

Should the threshold value be mid-point of the bin or the maximum?
Default is to return bin maximum.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::OtsuThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::ReturnBinMidpointOff "
";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::ReturnBinMidpointOn "

Set the value of ReturnBinMidpoint to true or false respectfully.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value. The default value NumericTraits<OutputPixelType>::max()

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins. Defaults is 128.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::SetReturnBinMidpoint "

Should the threshold value be mid-point of the bin or the maximum?
Default is to return bin maximum.

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::OtsuThresholdImageFilter::~OtsuThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::PasteImageFilter "
C++ includes: sitkPasteImageFilter.h
";

%feature("docstring")  itk::simple::PasteImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::PasteImageFilter::Execute "
";

%feature("docstring")  itk::simple::PasteImageFilter::Execute "
";

%feature("docstring")  itk::simple::PasteImageFilter::Execute "
";

%feature("docstring")  itk::simple::PasteImageFilter::GetDestinationIndex "

Set/Get the destination index (where in the first input the second
input will be pasted.

";

%feature("docstring")  itk::simple::PasteImageFilter::GetDestinationSkipAxes "

Set/Get the array describing which axes in the destination image to
skip

The axes with true values are set to 1, to fill the difference between
the dimension of the input and source image. The number of true values
in DestinationSkipAxes plus the DestinationImageDimension must equal
the InputImageDimension.

By default this array contains SourceImageDimension false values
followed by true values for the remainder.

";

%feature("docstring")  itk::simple::PasteImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::PasteImageFilter::GetSourceIndex "
";

%feature("docstring")  itk::simple::PasteImageFilter::GetSourceSize "
";

%feature("docstring")  itk::simple::PasteImageFilter::PasteImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::PasteImageFilter::SetDestinationIndex "

Set/Get the destination index (where in the first input the second
input will be pasted.

";

%feature("docstring")  itk::simple::PasteImageFilter::SetDestinationSkipAxes "

Set/Get the array describing which axes in the destination image to
skip

The axes with true values are set to 1, to fill the difference between
the dimension of the input and source image. The number of true value
in DestinationSkipAxes plus the DestinationImageDimension must equal
the InputImageDimension.

By default this array contains SourceImageDimension false values
followed by true values for the remainder.

";

%feature("docstring")  itk::simple::PasteImageFilter::SetSourceIndex "
";

%feature("docstring")  itk::simple::PasteImageFilter::SetSourceSize "
";

%feature("docstring")  itk::simple::PasteImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::PasteImageFilter::~PasteImageFilter "

Destructor

";


%feature("docstring") itk::simple::PatchBasedDenoisingImageFilter "

Derived class implementing a specific patch-based denoising algorithm,
as detailed below.


This class is derived from the base class PatchBasedDenoisingBaseImageFilter; please refer to the documentation of the base class first. This
class implements a denoising filter that uses iterative non-local, or
semi-local, weighted averaging of image patches for image denoising.
The intensity at each pixel 'p' gets updated as a weighted average of
intensities of a chosen subset of pixels from the image.

This class implements the denoising algorithm using a Gaussian kernel
function for nonparametric density estimation. The class implements a
scheme to automatically estimated the kernel bandwidth parameter
(namely, sigma) using leave-one-out cross validation. It implements
schemes for random sampling of patches non-locally (from the entire
image) as well as semi-locally (from the spatial proximity of the
pixel being denoised at the specific point in time). It implements a
specific scheme for defining patch weights (mask) as described in
Awate and Whitaker 2005 IEEE CVPR and 2006 IEEE TPAMI.


See:
 PatchBasedDenoisingBaseImageFilter

 itk::PatchBasedDenoisingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkPatchBasedDenoisingImageFilter.h
";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::AlwaysTreatComponentsAsEuclideanOff "
";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::AlwaysTreatComponentsAsEuclideanOn "

Set the value of AlwaysTreatComponentsAsEuclidean to true or false
respectfully.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetAlwaysTreatComponentsAsEuclidean "

Set/Get flag indicating whether all components should always be
treated as if they are in euclidean space regardless of pixel type.
Defaults to false.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetKernelBandwidthEstimation "

Set/Get flag indicating whether kernel-bandwidth should be estimated
automatically from the image data. Defaults to true.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetKernelBandwidthFractionPixelsForEstimation "

Set/Get the fraction of the image to use for kernel bandwidth sigma
estimation. To reduce the computational burden for computing sigma, a
small random fraction of the image pixels can be used.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetKernelBandwidthMultiplicationFactor "

Set/Get the kernel bandwidth sigma multiplication factor used to
modify the automatically-estimated kernel bandwidth sigma. At times,
it may be desirable to modify the value of the automatically-estimated
sigma. Typically, this number isn't very far from 1. Note: This is
used only when KernelBandwidthEstimation is True/On.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetKernelBandwidthSigma "

Set/Get initial kernel bandwidth estimate. To prevent the class from
automatically modifying this estimate, set KernelBandwidthEstimation
to false in the base class.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetKernelBandwidthUpdateFrequency "

Set/Get the update frequency for the kernel bandwidth estimation. An
optimal bandwidth will be re-estimated based on the denoised image
after every 'n' iterations. Must be a positive integer. Defaults to 3,
i.e. bandwidth updated after every 3 denoising iteration.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetNoiseModel "

Set/Get the noise model type. Defaults to GAUSSIAN. To use the noise
model during denoising, FidelityWeight must be positive.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetNoiseModelFidelityWeight "

Set/Get the weight on the fidelity term (penalizes deviations from the
noisy data). This option is used when a noise model is specified. This
weight controls the balance between the smoothing and the closeness to
the noisy data.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetNoiseSigma "
";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetNumberOfIterations "

Set/Get the number of denoising iterations to perform. Must be a
positive integer. Defaults to 1.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetNumberOfSamplePatches "
";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetPatchRadius "

Set/Get the patch radius specified in physical coordinates. Patch
radius is preferably set to an even number. Currently, only isotropic
patches in physical space are allowed; patches can be anisotropic in
voxel space.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::GetSampleVariance "

Set/Get the variance of the domain where patches are sampled.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::KernelBandwidthEstimationOff "
";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::KernelBandwidthEstimationOn "

Set the value of KernelBandwidthEstimation to true or false
respectfully.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::PatchBasedDenoisingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetAlwaysTreatComponentsAsEuclidean "

Set/Get flag indicating whether all components should always be
treated as if they are in euclidean space regardless of pixel type.
Defaults to false.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetKernelBandwidthEstimation "

Set/Get flag indicating whether kernel-bandwidth should be estimated
automatically from the image data. Defaults to true.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetKernelBandwidthFractionPixelsForEstimation "

Set/Get the fraction of the image to use for kernel bandwidth sigma
estimation. To reduce the computational burden for computing sigma, a
small random fraction of the image pixels can be used.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetKernelBandwidthMultiplicationFactor "

Set/Get the kernel bandwidth sigma multiplication factor used to
modify the automatically-estimated kernel bandwidth sigma. At times,
it may be desirable to modify the value of the automatically-estimated
sigma. Typically, this number isn't very far from 1. Note: This is
used only when KernelBandwidthEstimation is True/On.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetKernelBandwidthSigma "

Set/Get initial kernel bandwidth estimate. To prevent the class from
automatically modifying this estimate, set KernelBandwidthEstimation
to false in the base class.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetKernelBandwidthUpdateFrequency "

Set/Get the update frequency for the kernel bandwidth estimation. An
optimal bandwidth will be re-estimated based on the denoised image
after every 'n' iterations. Must be a positive integer. Defaults to 3,
i.e. bandwidth updated after every 3 denoising iteration.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetNoiseModel "

Set/Get the noise model type. Defaults to GAUSSIAN. To use the noise
model during denoising, FidelityWeight must be positive.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetNoiseModelFidelityWeight "

Set/Get the weight on the fidelity term (penalizes deviations from the
noisy data). This option is used when a noise model is specified. This
weight controls the balance between the smoothing and the closeness to
the noisy data.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetNoiseSigma "

Set/Get the noise sigma. Used by the noise model where appropriate,
defaults to 5% of the image intensity range

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetNumberOfIterations "

Set/Get the number of denoising iterations to perform. Must be a
positive integer. Defaults to 1.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetNumberOfSamplePatches "

Set/Get the number of patches to sample for each pixel.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetPatchRadius "

Set/Get the patch radius specified in physical coordinates. Patch
radius is preferably set to an even number. Currently, only isotropic
patches in physical space are allowed; patches can be anisotropic in
voxel space.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::SetSampleVariance "

Set/Get the variance of the domain where patches are sampled.

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::PatchBasedDenoisingImageFilter::~PatchBasedDenoisingImageFilter "

Destructor

";


%feature("docstring") itk::simple::PermuteAxesImageFilter "

Permutes the image axes according to a user specified order.


PermuateAxesImageFilter permutes the image axes according to a user
specified order. The permutation order is set via method SetOrder(
order ) where the input is an array of ImageDimension number of
unsigned int. The elements of the array must be a rearrangment of the
numbers from 0 to ImageDimension - 1.

The i-th axis of the output image corresponds with the order[i]-th
axis of the input image.

The output meta image information (LargestPossibleRegion, spacing,
origin) is computed by permuting the corresponding input meta
information.
See:
 itk::simple::PermuteAxes for the procedural interface

 itk::PermuteAxesImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkPermuteAxesImageFilter.h
";

%feature("docstring")  itk::simple::PermuteAxesImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::PermuteAxesImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::PermuteAxesImageFilter::GetOrder "

Get the permutation order.

";

%feature("docstring")  itk::simple::PermuteAxesImageFilter::PermuteAxesImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::PermuteAxesImageFilter::SetOrder "

Set the permutation order. The elements of order must be a
rearrangement of the numbers from 0 to ImageDimension - 1.

";

%feature("docstring")  itk::simple::PermuteAxesImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::PermuteAxesImageFilter::~PermuteAxesImageFilter "

Destructor

";


%feature("docstring") itk::simple::PhysicalPointImageSource "

Generate an image of the physical locations of each pixel.


This image source supports image which have a multi-component pixel
equal to the image dimension, and variable length VectorImages. It is
recommended that the component type be a real valued type.
See:
 itk::simple::PhysicalPointSource for the procedural interface

 itk::PhysicalPointImageSource for the Doxygen on the original ITK class.


C++ includes: sitkPhysicalPointImageSource.h
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::PhysicalPointImageSource::GetDirection "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::GetName "

Name of this class

";

%feature("docstring")  itk::simple::PhysicalPointImageSource::GetOrigin "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::GetOutputPixelType "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::GetSize "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::GetSpacing "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::PhysicalPointImageSource "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::PhysicalPointImageSource::SetDirection "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::SetOrigin "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::SetOutputPixelType "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::SetReferenceImage "

This methods sets the size, origin, spacing and direction to that of
the provided image

";

%feature("docstring")  itk::simple::PhysicalPointImageSource::SetSize "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::SetSpacing "
";

%feature("docstring")  itk::simple::PhysicalPointImageSource::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::PhysicalPointImageSource::~PhysicalPointImageSource "

Destructor

";


%feature("docstring") itk::simple::PimpleImageBase "

Private implementation idiom image base class.


We utilize the private implementation ( or PImple) programming idiom
to modify the behavior of the simple image class based on the
different image types.

This class is designed to utilize the trivial copy, and assgnement
operators

C++ includes: sitkPimpleImageBase.h
";

%feature("docstring")  itk::simple::PimpleImageBase::DeepCopy "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsDouble "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsDouble "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsFloat "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsFloat "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsUInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsUInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsUInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsUInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsUInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsUInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsUInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsUInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsVoid "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetBufferAsVoid "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetDataBase "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetDataBase "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetDepth "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetDimension "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetDirection "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetHeight "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetNumberOfComponentsPerPixel "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetNumberOfPixels "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetOrigin "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsComplexFloat32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsComplexFloat64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsDouble "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsFloat "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsUInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsUInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsUInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsUInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorFloat32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorFloat64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorUInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorUInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorUInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelAsVectorUInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetPixelID "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetReferenceCountOfImage "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetSize "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetSize "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetSpacing "
";

%feature("docstring")  itk::simple::PimpleImageBase::GetWidth "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetDirection "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetOrigin "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsComplexFloat32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsComplexFloat64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsDouble "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsFloat "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsUInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsUInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsUInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsUInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorFloat32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorFloat64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorUInt16 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorUInt32 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorUInt64 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetPixelAsVectorUInt8 "
";

%feature("docstring")  itk::simple::PimpleImageBase::SetSpacing "
";

%feature("docstring")  itk::simple::PimpleImageBase::ShallowCopy "
";

%feature("docstring")  itk::simple::PimpleImageBase::ToString "
";

%feature("docstring")  itk::simple::PimpleImageBase::TransformContinuousIndexToPhysicalPoint "
";

%feature("docstring")  itk::simple::PimpleImageBase::TransformIndexToPhysicalPoint "
";

%feature("docstring")  itk::simple::PimpleImageBase::TransformPhysicalPointToContinuousIndex "
";

%feature("docstring")  itk::simple::PimpleImageBase::TransformPhysicalPointToIndex "
";

%feature("docstring")  itk::simple::PimpleImageBase::~PimpleImageBase "
";


%feature("docstring") itk::simple::PowImageFilter "

Computes the powers of 2 images.


This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.

The output of the pow function will be cast to the pixel type of the
output image.

The total operation over one pixel will be

The pow function can be applied to two images with the following:

Additionally, this filter can be used to raise every pixel of an image
to a power of a constant by using
See:
 itk::simple::Pow for the procedural interface

 itk::PowImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkPowImageFilter.h
";

%feature("docstring")  itk::simple::PowImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::PowImageFilter::Execute "
";

%feature("docstring")  itk::simple::PowImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::PowImageFilter::Execute "
";

%feature("docstring")  itk::simple::PowImageFilter::Execute "
";

%feature("docstring")  itk::simple::PowImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::PowImageFilter::PowImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::PowImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::PowImageFilter::~PowImageFilter "

Destructor

";


%feature("docstring") itk::simple::ProcessObject "

Base class for SimpleITK classes based on ProcessObject.

C++ includes: sitkProcessObject.h
";

%feature("docstring")  itk::simple::ProcessObject::Abort "

Sets an abort flag on the active process.

Requests the current active process to abort. Additional, progress or
iteration event may occur. If aborted then, an AbortEvent should
occur. The Progress should be set to 1.0 after aborting.

The expected behavior is that not exception should be throw out of
this processes Execute method. Additionally, the results returned are
valid but undefined content. The content may be only partially
updated, uninitialized or the a of size zero.

If there is no active process the method has no effect.

";

%feature("docstring")  itk::simple::ProcessObject::AddCommand "

Add a Command Object to observer the event.


The Command object's Execute method will be invoked when the internal ITK Object has the event. These events only occur during this ProcessObject's Execute method when the ITK filter is running. The command occurs
in the same thread as this objects Execute methods was called in.

An internal reference is made between the Command and this ProcessObject which enable automatic removal of the command when deleted. This
enables both object to exist as stack based object and be
automatically cleaned up.

Unless specified otherwise, it's safe to get any value during
execution. \"Measurements\" will have valid values only after the
Execute method has returned. \"Active Measurements\" will have valid
values during events, and access the underlying ITK object.

Deleting a command this object has during a command call-back will
produce undefined behavior.

For more information see the page CommandPage.


The return value is reserved for latter usage.


";

%feature("docstring")  itk::simple::ProcessObject::AddCommand "

Directly add a callback to observe an event.


This overloaded method can take a C++ lambda function as a second
argument.

";

%feature("docstring")  itk::simple::ProcessObject::GetName "

return user readable name for the filter

";

%feature("docstring")  itk::simple::ProcessObject::GetProgress "

An Active Measurement of the progress of execution.


Get the execution progress of the current process object. The progress
is a floating number in [0,1] with 0 meaning no progress and 1 meaning
the filter has completed execution (or aborted).

This is an Active Measurement so it can be accessed during Events
during the execution.

";

%feature("docstring")  itk::simple::ProcessObject::HasCommand "

Query of this object has any registered commands for event.

";

%feature("docstring")  itk::simple::ProcessObject::ProcessObject "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ProcessObject::RemoveAllCommands "

Remove all registered commands.


Calling when this object is invoking anther command will produce
undefined behavior.

";

%feature("docstring")  itk::simple::ProcessObject::ToString "
";

%feature("docstring")  itk::simple::ProcessObject::~ProcessObject "

Default Destructor

";


%feature("docstring") itk::simple::ProjectedLandweberDeconvolutionImageFilter "

Deconvolve an image using the projected Landweber deconvolution
algorithm.


This filter performs the same calculation per iteration as the LandweberDeconvolutionImageFilter . However, at each iteration, negative pixels in the intermediate
result are projected (set) to zero. This is useful if the solution is
assumed to always be non-negative, which is the case when dealing with
images formed by counting photons, for example.

This code was adapted from the Insight Journal contribution:

\"Deconvolution: infrastructure and reference algorithms\" by Gaetan
Lehmann https://hdl.handle.net/10380/3207


Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France

Cory Quammen, The University of North Carolina at Chapel Hill

See:
 IterativeDeconvolutionImageFilter

 RichardsonLucyDeconvolutionImageFilter

 LandweberDeconvolutionImageFilter

 itk::simple::ProjectedLandweberDeconvolution for the procedural interface

 itk::ProjectedLandweberDeconvolutionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkProjectedLandweberDeconvolutionImageFilter.h
";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::GetAlpha "

Get the relaxation factor.

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::GetBoundaryCondition "
";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::GetNormalize "
";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::GetNumberOfIterations "

Get the number of iterations.

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::GetOutputRegionMode "
";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::NormalizeOff "
";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::NormalizeOn "

Set the value of Normalize to true or false respectfully.

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::ProjectedLandweberDeconvolutionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::SetAlpha "

Set the relaxation factor.

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::SetBoundaryCondition "
";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::SetNormalize "

Normalize the output image by the sum of the kernel components

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::SetNumberOfIterations "

Set the number of iterations.

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::SetOutputRegionMode "
";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ProjectedLandweberDeconvolutionImageFilter::~ProjectedLandweberDeconvolutionImageFilter "

Destructor

";


%feature("docstring") itk::simple::RankImageFilter "

Rank filter of a greyscale image.


Nonlinear filter in which each output pixel is a user defined rank of
input pixels in a user defined neighborhood. The default rank is 0.5
(median). The boundary conditions are different to the standard
itkMedianImageFilter. In this filter the neighborhood is cropped at
the boundary, and is therefore smaller.

This filter uses a recursive implementation - essentially the one by
Huang 1979, I believe, to compute the rank, and is therefore usually a
lot faster than the direct implementation. The extensions to Huang are
support for arbitrary pixel types (using c++ maps) and arbitrary
neighborhoods. I presume that these are not new ideas.

This filter is based on the sliding window code from the
consolidatedMorphology package on InsightJournal.

The structuring element is assumed to be composed of binary values
(zero or one). Only elements of the structuring element having values
> 0 are candidates for affecting the center pixel.

This code was contributed in the Insight Journal paper: \"Efficient
implementation of kernel filtering\" by Beare R., Lehmann G https://hdl.handle.net/1926/555 http://www.insight-journal.org/browse/publication/160


See:
 MedianImageFilter

Richard Beare

See:
 itk::simple::Rank for the procedural interface

 itk::RankImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRankImageFilter.h
";

%feature("docstring")  itk::simple::RankImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RankImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RankImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::RankImageFilter::GetRank "
";

%feature("docstring")  itk::simple::RankImageFilter::RankImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RankImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::RankImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::RankImageFilter::SetRank "
";

%feature("docstring")  itk::simple::RankImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RankImageFilter::~RankImageFilter "

Destructor

";


%feature("docstring") itk::simple::RealAndImaginaryToComplexImageFilter "

ComposeImageFilter combine several scalar images into a multicomponent image.


ComposeImageFilter combine several scalar images into an itk::Image of vector pixel ( itk::Vector , itk::RGBPixel , ...), of std::complex pixel, or in an itk::VectorImage .

Inputs and Usage
 All input images are expected to have the same template parameters
and have the same size and origin.

See:
 VectorImage

 VectorIndexSelectionCastImageFilter

 itk::simple::RealAndImaginaryToComplex for the procedural interface

 itk::ComposeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRealAndImaginaryToComplexImageFilter.h
";

%feature("docstring")  itk::simple::RealAndImaginaryToComplexImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::RealAndImaginaryToComplexImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RealAndImaginaryToComplexImageFilter::RealAndImaginaryToComplexImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RealAndImaginaryToComplexImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RealAndImaginaryToComplexImageFilter::~RealAndImaginaryToComplexImageFilter "

Destructor

";


%feature("docstring") itk::simple::RealToHalfHermitianForwardFFTImageFilter "

Base class for specialized real-to-complex forward Fast Fourier Transform .


This is a base class for the \"forward\" or \"direct\" discrete
Fourier Transform . This is an abstract base class: the actual implementation is
provided by the best child class available on the system when the
object is created via the object factory system.

This class transforms a real input image into its complex Fourier
transform. The Fourier transform of a real input image has Hermitian
symmetry: $ f(\\\\mathbf{x}) = f^*(-\\\\mathbf{x}) $ . That is, when the result of the transform is split in half along
the X-dimension, the values in the second half of the transform are
the complex conjugates of values in the first half reflected about the
center of the image in each dimension. This filter takes advantage of
the Hermitian symmetry property and reduces the size of the output in
the first dimension to N/2+1, where N is the size of the input image
in that dimension and the division by 2 is rounded down.


See:
 HalfHermitianToRealInverseFFTImageFilter

 ForwardFFTImageFilter

 itk::simple::RealToHalfHermitianForwardFFT for the procedural interface

 itk::RealToHalfHermitianForwardFFTImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRealToHalfHermitianForwardFFTImageFilter.h
";

%feature("docstring")  itk::simple::RealToHalfHermitianForwardFFTImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RealToHalfHermitianForwardFFTImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RealToHalfHermitianForwardFFTImageFilter::RealToHalfHermitianForwardFFTImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RealToHalfHermitianForwardFFTImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RealToHalfHermitianForwardFFTImageFilter::~RealToHalfHermitianForwardFFTImageFilter "

Destructor

";


%feature("docstring") itk::simple::ReconstructionByDilationImageFilter "

grayscale reconstruction by dilation of an image


Reconstruction by dilation operates on a \"marker\" image and a
\"mask\" image, and is defined as the dilation of the marker image
with respect to the mask image iterated until stability.

The marker image must be less than or equal to the mask image (on a
pixel by pixel basis).

Geodesic morphology is described in Chapter 6.2 of Pierre Soille's
book \"Morphological Image Analysis: Principles and Applications\",
Second Edition, Springer, 2003.

Algorithm implemented in this filter is based on algorithm described
by Kevin Robinson and Paul F. Whelan in \"Efficient Morphological
Reconstruction: A Downhill Filter\", Pattern Recognition Letters,
Volume 25, Issue 15, November 2004, Pages 1759-1767.

The algorithm, a description of the transform and some applications
can be found in \"Morphological Grayscale Reconstruction in Image
Analysis:  Applications and Efficient Algorithms\", Luc Vincent, IEEE
Transactions on image processing, Vol. 2, April 1993.


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

See:
 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter , ReconstructionByErosionImageFilter , OpeningByReconstructionImageFilter , ClosingByReconstructionImageFilter , ReconstructionImageFilter

 itk::simple::ReconstructionByDilation for the procedural interface

 itk::ReconstructionByDilationImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkReconstructionByDilationImageFilter.h
";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::GetFullyConnected "
";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::GetUseInternalCopy "
";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::ReconstructionByDilationImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::SetFullyConnected "
";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::SetUseInternalCopy "
";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::UseInternalCopyOff "
";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::UseInternalCopyOn "

Set the value of UseInternalCopy to true or false respectfully.

";

%feature("docstring")  itk::simple::ReconstructionByDilationImageFilter::~ReconstructionByDilationImageFilter "

Destructor

";


%feature("docstring") itk::simple::ReconstructionByErosionImageFilter "

grayscale reconstruction by erosion of an image


Reconstruction by erosion operates on a \"marker\" image and a
\"mask\" image, and is defined as the erosion of the marker image with
respect to the mask image iterated until stability.

The marker image must be less than or equal to the mask image (on a
pixel by pixel basis).

Geodesic morphology is described in Chapter 6.2 of Pierre Soille's
book \"Morphological Image Analysis: Principles and Applications\",
Second Edition, Springer, 2003.

Algorithm implemented in this filter is based on algorithm described
by Kevin Robinson and Paul F. Whelan in \"Efficient Morphological
Reconstruction: A Downhill Filter\", Pattern Recognition Letters,
Volume 25, Issue 15, November 2004, Pages 1759-1767.

The algorithm, a description of the transform and some applications
can be found in \"Morphological Grayscale Reconstruction in Image
Analysis:  Applications and Efficient Algorithms\", Luc Vincent, IEEE
Transactions on image processing, Vol. 2, April 1993.


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

See:
 MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleFunctionDilateImageFilter , BinaryDilateImageFilter , ReconstructionByErosionImageFilter , OpeningByReconstructionImageFilter , ClosingByReconstructionImageFilter , ReconstructionImageFilter

 itk::simple::ReconstructionByErosion for the procedural interface

 itk::ReconstructionByErosionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkReconstructionByErosionImageFilter.h
";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::GetFullyConnected "
";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::GetUseInternalCopy "
";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::ReconstructionByErosionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::SetFullyConnected "
";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::SetUseInternalCopy "
";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::UseInternalCopyOff "
";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::UseInternalCopyOn "

Set the value of UseInternalCopy to true or false respectfully.

";

%feature("docstring")  itk::simple::ReconstructionByErosionImageFilter::~ReconstructionByErosionImageFilter "

Destructor

";


%feature("docstring") itk::simple::RecursiveGaussianImageFilter "

Base class for computing IIR convolution with an approximation of a
Gaussian kernel.


\\\\[ \\\\frac{ 1 }{ \\\\sigma \\\\sqrt{ 2 \\\\pi } } \\\\exp{
\\\\left( - \\\\frac{x^2}{ 2 \\\\sigma^2 } \\\\right) } \\\\]

RecursiveGaussianImageFilter is the base class for recursive filters that approximate convolution
with the Gaussian kernel. This class implements the recursive
filtering method proposed by R.Deriche in IEEE-PAMI Vol.12, No.1,
January 1990, pp 78-87, \"Fast Algorithms for Low-Level Vision\"

Details of the implementation are described in the technical report: R.
Deriche, \"Recursively Implementing The Gaussian and Its
Derivatives\", INRIA, 1993, ftp://ftp.inria.fr/INRIA/tech-reports/RR/RR-1893.ps.gz

Further improvements of the algorithm are described in: G. Farneback &
C.-F. Westin, \"On Implementation of Recursive Gaussian  Filters\", so
far unpublished.

As compared to itk::DiscreteGaussianImageFilter , this filter tends to be faster for large kernels, and it can take
the derivative of the blurred image in one step. Also, note that we
have itk::RecursiveGaussianImageFilter::SetSigma() , but itk::DiscreteGaussianImageFilter::SetVariance() .


See:
 DiscreteGaussianImageFilter

 itk::simple::RecursiveGaussian for the procedural interface

 itk::RecursiveGaussianImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRecursiveGaussianImageFilter.h
";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::Execute "
";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::GetDirection "
";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::GetNormalizeAcrossScale "
";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::GetOrder "

Set/Get the Order of the Gaussian to convolve with.


ZeroOrder is equivalent to convolving with a Gaussian. This is the
default.

FirstOrder is equivalent to convolving with the first derivative of a
Gaussian.

SecondOrder is equivalent to convolving with the second derivative of
a Gaussian.


";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::GetSigma "

Set/Get the Sigma, measured in world coordinates, of the Gaussian
kernel. The default is 1.0. An exception will be generated if the
Sigma value is less than or equal to zero.

";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::NormalizeAcrossScaleOff "
";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::NormalizeAcrossScaleOn "

Set the value of NormalizeAcrossScale to true or false respectfully.

";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::RecursiveGaussianImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::SetDirection "
";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::SetNormalizeAcrossScale "

Set/Get the flag for normalizing the gaussian over scale-space.

This flag enables the analysis of the differential shape of features
independent of their size ( both pixels and physical size ). Following
the notation of Tony Lindeberg:

Let \\\\[ L(x; t) = g(x; t) \\\\ast f(x) \\\\] be the scale-space representation of image \\\\[ f(x) \\\\] where \\\\[ g(x; t) = \\\\frac{1}{ \\\\sqrt{ 2 \\\\pi t} } \\\\exp{
\\\\left( -\\\\frac{x^2}{ 2 t } \\\\right) } \\\\] is the Gaussian function and \\\\[\\\\ast\\\\] denotes convolution. This is a change from above with \\\\[ t = \\\\sigma^2 \\\\] .

Then the normalized derivative operator for normalized coordinates
across scale is:

\\\\[ \\\\partial_\\\\xi = \\\\sqrt{t} \\\\partial_x \\\\]

The resulting scaling factor is \\\\[ \\\\sigma^N \\\\] where N is the order of the derivative.

When this flag is ON the filter will be normalized in such a way that
the values of derivatives are not biased by the size of the object.
That is to say the maximum value a feature reaches across scale is
independent of the scale of the object.

For analyzing an image across scale-space you want to enable this
flag. It is disabled by default.


Not all scale space axioms are satisfied by this filter, some are only
approximated. Particularly, at fine scales ( say less than 1 pixel )
other methods such as a discrete Gaussian kernel should be considered.


";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::SetOrder "

Set/Get the Order of the Gaussian to convolve with.


ZeroOrder is equivalent to convolving with a Gaussian. This is the
default.

FirstOrder is equivalent to convolving with the first derivative of a
Gaussian.

SecondOrder is equivalent to convolving with the second derivative of
a Gaussian.


";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::SetSigma "

Set/Get the Sigma, measured in world coordinates, of the Gaussian
kernel. The default is 1.0. An exception will be generated if the
Sigma value is less than or equal to zero.

";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RecursiveGaussianImageFilter::~RecursiveGaussianImageFilter "

Destructor

";


%feature("docstring") itk::simple::RegionOfInterestImageFilter "

Extract a region of interest from the input image.


This filter produces an output image of the same dimension as the
input image. The user specifies the region of the input image that
will be contained in the output image. The origin coordinates of the
output images will be computed in such a way that if mapped to
physical space, the output image will overlay the input image with
perfect registration. In other words, a registration process between
the output image and the input image will return an identity
transform.

If you are interested in changing the dimension of the image, you may
want to consider the ExtractImageFilter . For example for extracting a 2D image from a slice of a 3D image.

The region to extract is set using the method SetRegionOfInterest.


See:
 ExtractImageFilter

 itk::simple::RegionOfInterest for the procedural interface

 itk::RegionOfInterestImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRegionOfInterestImageFilter.h
";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::GetIndex "
";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::GetSize "
";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::RegionOfInterestImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::SetIndex "

Set the inclusive starting index of the region extracted.

";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::SetRegionOfInterest "

Sets the region extracted by a single array of the starting indexes
followed by the sizes in pixels.

";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::SetSize "

Size in pixels of the region extracted.

";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RegionOfInterestImageFilter::~RegionOfInterestImageFilter "

Destructor

";


%feature("docstring") itk::simple::RegionalMaximaImageFilter "

Produce a binary image where foreground is the regional maxima of the
input image.


Regional maxima are flat zones surrounded by pixels of lower value.

If the input image is constant, the entire image can be considered as
a maxima or not. The desired behavior can be selected with the SetFlatIsMaxima() method.


Gaetan Lehmann
 This class was contributed to the Insight Journal by author Gaetan
Lehmann. Biologie du Developpement et de la Reproduction, INRA de
Jouy-en-Josas, France. The paper can be found at https://hdl.handle.net/1926/153


See:
 ValuedRegionalMaximaImageFilter

 HConvexImageFilter

 RegionalMinimaImageFilter

 itk::simple::RegionalMaxima for the procedural interface

 itk::RegionalMaximaImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRegionalMaximaImageFilter.h
";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::FlatIsMaximaOff "
";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::FlatIsMaximaOn "

Set the value of FlatIsMaxima to true or false respectfully.

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::GetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::GetFlatIsMaxima "

Set/Get wether a flat image must be considered as a maxima or not.
Defaults to true.

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::GetForegroundValue "

Set/Get the value in the output image to consider as \"foreground\".
Defaults to maximum value of PixelType.

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::RegionalMaximaImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::SetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::SetFlatIsMaxima "

Set/Get whether a flat image must be considered as a maxima or not.
Defaults to true.

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::SetForegroundValue "

Set/Get the value in the output image to consider as \"foreground\".
Defaults to maximum value of PixelType.

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RegionalMaximaImageFilter::~RegionalMaximaImageFilter "

Destructor

";


%feature("docstring") itk::simple::RegionalMinimaImageFilter "

Produce a binary image where foreground is the regional minima of the
input image.


Regional minima are flat zones surrounded by pixels of greater value.

If the input image is constant, the entire image can be considered as
a minima or not. The SetFlatIsMinima() method let the user choose which behavior to use.

This class was contributed to the Insight Journal by
Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France. https://hdl.handle.net/1926/153

See:
 RegionalMaximaImageFilter

 ValuedRegionalMinimaImageFilter

 HConcaveImageFilter

 itk::simple::RegionalMinima for the procedural interface

 itk::RegionalMinimaImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRegionalMinimaImageFilter.h
";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::FlatIsMinimaOff "
";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::FlatIsMinimaOn "

Set the value of FlatIsMinima to true or false respectfully.

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::GetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::GetFlatIsMinima "

Set/Get wether a flat image must be considered as a minima or not.
Defaults to true.

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::GetForegroundValue "

Set/Get the value in the output image to consider as \"foreground\".
Defaults to maximum value of PixelType.

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::GetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::RegionalMinimaImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::SetBackgroundValue "

Set/Get the value used as \"background\" in the output image. Defaults
to NumericTraits<PixelType>::NonpositiveMin() .

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::SetFlatIsMinima "

Set/Get wether a flat image must be considered as a minima or not.
Defaults to true.

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::SetForegroundValue "

Set/Get the value in the output image to consider as \"foreground\".
Defaults to maximum value of PixelType.

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::SetFullyConnected "

Set/Get whether the connected components are defined strictly by face
connectivity or by face+edge+vertex connectivity. Default is
FullyConnectedOff. For objects that are 1 pixel wide, use
FullyConnectedOn.

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RegionalMinimaImageFilter::~RegionalMinimaImageFilter "

Destructor

";


%feature("docstring") itk::simple::RelabelComponentImageFilter "

Relabel the components in an image such that consecutive labels are
used.


RelabelComponentImageFilter remaps the labels associated with the objects in an image (as from
the output of ConnectedComponentImageFilter ) such that the label numbers are consecutive with no gaps between
the label numbers used. By default, the relabeling will also sort the
labels based on the size of the object: the largest object will have
label #1, the second largest will have label #2, etc. If two labels
have the same size their initial order is kept. The sorting by size
can be disabled using SetSortByObjectSize.

Label #0 is assumed to be the background and is left unaltered by the
relabeling.

RelabelComponentImageFilter is typically used on the output of the ConnectedComponentImageFilter for those applications that want to extract the largest object or the
\"k\" largest objects. Any particular object can be extracted from the
relabeled output using a BinaryThresholdImageFilter . A group of objects can be extracted from the relabled output using
a ThresholdImageFilter .

Once all the objects are relabeled, the application can query the
number of objects and the size of each object. Object sizes are returned in a vector. The size of the background is not
calculated. So the size of object #1 is GetSizeOfObjectsInPixels() [0], the size of object #2 is GetSizeOfObjectsInPixels() [1], etc.

If user sets a minimum object size, all objects with fewer pixels than
the minimum will be discarded, so that the number of objects reported
will be only those remaining. The GetOriginalNumberOfObjects method
can be called to find out how many objects were present before the
small ones were discarded.

RelabelComponentImageFilter can be run as an \"in place\" filter, where it will overwrite its
output. The default is run out of place (or generate a separate
output). \"In place\" operation can be controlled via methods in the
superclass, InPlaceImageFilter::InPlaceOn() and InPlaceImageFilter::InPlaceOff() .


See:
 ConnectedComponentImageFilter , BinaryThresholdImageFilter , ThresholdImageFilter

 itk::simple::RelabelComponent for the procedural interface

 itk::RelabelComponentImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRelabelComponentImageFilter.h
";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::Execute "
";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::GetMinimumObjectSize "

Get the caller-defined minimum size of an object in pixels. If the
caller has not set the minimum, 0 will be returned, which is to be
interpreted as meaning that no minimum exists, and all objects in the
original label map will be passed through to the output.

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::GetNumberOfObjects "

Get the number of objects in the image. This information is only valid
after the filter has executed.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::GetOriginalNumberOfObjects "

Get the original number of objects in the image before small objects
were discarded. This information is only valid after the filter has
executed. If the caller has not specified a minimum object size,
OriginalNumberOfObjects is the same as NumberOfObjects.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::GetSizeOfObjectsInPhysicalUnits "

Get the size of each object in physical space (in units of pixel
size). This information is only valid after the filter has executed. Size of the background is not calculated. Size of object #1 is GetSizeOfObjectsInPhysicalUnits() [0]. Size of object #2 is GetSizeOfObjectsInPhysicalUnits() [1]. Etc.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::GetSizeOfObjectsInPixels "

Get the size of each object in pixels. This information is only valid
after the filter has executed. Size of the background is not calculated. Size of object #1 is GetSizeOfObjectsInPixels() [0]. Size of object #2 is GetSizeOfObjectsInPixels() [1]. Etc.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::GetSortByObjectSize "

Controls whether the object labels are sorted by size. If false,
initial order of labels is kept.

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::RelabelComponentImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::SetMinimumObjectSize "

Set the minimum size in pixels for an object. All objects smaller than
this size will be discarded and will not appear in the output label
map. NumberOfObjects will count only the objects whose pixel counts
are greater than or equal to the minimum size. Call
GetOriginalNumberOfObjects to find out how many objects were present
in the original label map.

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::SetSortByObjectSize "

Controls whether the object labels are sorted by size. If false,
initial order of labels is kept.

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::SortByObjectSizeOff "
";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::SortByObjectSizeOn "

Set the value of SortByObjectSize to true or false respectfully.

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RelabelComponentImageFilter::~RelabelComponentImageFilter "

Destructor

";


%feature("docstring") itk::simple::RelabelLabelMapFilter "

This filter relabels the LabelObjects; the new labels are arranged
consecutively with consideration for the background value.


This filter takes the LabelObjects from the input and reassigns them
to the output by calling the PushLabelObject method, which by default,
attempts to reorganize the labels consecutively. The user can assign
an arbitrary value to the background; the filter will assign the
labels consecutively by skipping the background value.

This implementation was taken from the Insight Journal paper: https://hdl.handle.net/1926/584 or http://www.insight-journal.org/browse/publication/176
Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ShapeLabelObject , RelabelComponentImageFilter

 itk::simple::RelabelLabelMapFilter for the procedural interface

 itk::RelabelLabelMapFilter for the Doxygen on the original ITK class.


C++ includes: sitkRelabelLabelMapFilter.h
";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::Execute "
";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::GetReverseOrdering "
";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::RelabelLabelMapFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::ReverseOrderingOff "
";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::ReverseOrderingOn "

Set the value of ReverseOrdering to true or false respectfully.

";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::SetReverseOrdering "
";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RelabelLabelMapFilter::~RelabelLabelMapFilter "

Destructor

";


%feature("docstring") itk::simple::RenyiEntropyThresholdImageFilter "

Threshold an image using the RenyiEntropy Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the RenyiEntropyThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::RenyiEntropyThreshold for the procedural interface

 itk::RenyiEntropyThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRenyiEntropyThresholdImageFilter.h
";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::RenyiEntropyThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RenyiEntropyThresholdImageFilter::~RenyiEntropyThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::ResampleImageFilter "

Resample an image via a coordinate transform.


ResampleImageFilter resamples an existing image through some coordinate transform,
interpolating via some image function. The class is templated over the
types of the input and output images.

Note that the choice of interpolator function can be important. This
function is set via SetInterpolator() . The default is LinearInterpolateImageFunction <InputImageType, TInterpolatorPrecisionType>, which is reasonable for
ordinary medical images. However, some synthetic images have pixels
drawn from a finite prescribed set. An example would be a mask
indicating the segmentation of a brain into a small number of tissue
types. For such an image, one does not want to interpolate between
different pixel values, and so NearestNeighborInterpolateImageFunction < InputImageType, TCoordRep > would be a better choice.

If an sample is taken from outside the image domain, the default
behavior is to use a default pixel value. If different behavior is
desired, an extrapolator function can be set with SetExtrapolator() .

Output information (spacing, size and direction) for the output image
should be set. This information has the normal defaults of unit
spacing, zero origin and identity direction. Optionally, the output
information can be obtained from a reference image. If the reference
image is provided and UseReferenceImage is On, then the spacing,
origin and direction of the reference image will be used.

Since this filter produces an image which is a different size than its
input, it needs to override several of the methods defined in ProcessObject in order to properly manage the pipeline execution model. In
particular, this filter overrides ProcessObject::GenerateInputRequestedRegion() and ProcessObject::GenerateOutputInformation() .

This filter is implemented as a multithreaded filter. It provides a
DynamicThreadedGenerateData() method for its implementation.
WARNING:
For multithreading, the TransformPoint method of the user-designated
coordinate transform must be threadsafe.

See:
 itk::ResampleImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkResampleImageFilter.h
";

%feature("docstring")  itk::simple::ResampleImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ResampleImageFilter::GetDefaultPixelValue "

Get/Set the pixel value when a transformed pixel is outside of the
image. The default default pixel value is 0.

";

%feature("docstring")  itk::simple::ResampleImageFilter::GetInterpolator "

Get/Set the interpolator function. The default is LinearInterpolateImageFunction <InputImageType, TInterpolatorPrecisionType>. Some other options are NearestNeighborInterpolateImageFunction (useful for binary masks and other images with a small number of
possible pixel values), and BSplineInterpolateImageFunction (which provides a higher order of interpolation).

";

%feature("docstring")  itk::simple::ResampleImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ResampleImageFilter::GetOutputDirection "

Set the output direciton cosine matrix.

";

%feature("docstring")  itk::simple::ResampleImageFilter::GetOutputOrigin "

Get the output image origin.

";

%feature("docstring")  itk::simple::ResampleImageFilter::GetOutputPixelType "

Get the ouput pixel type.

";

%feature("docstring")  itk::simple::ResampleImageFilter::GetOutputSpacing "

Get the output image spacing.

";

%feature("docstring")  itk::simple::ResampleImageFilter::GetSize "

Get/Set the size of the output image.

";

%feature("docstring")  itk::simple::ResampleImageFilter::GetTransform "
";

%feature("docstring")  itk::simple::ResampleImageFilter::GetUseNearestNeighborExtrapolator "
";

%feature("docstring")  itk::simple::ResampleImageFilter::ResampleImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ResampleImageFilter::SetDefaultPixelValue "

Get/Set the pixel value when a transformed pixel is outside of the
image. The default default pixel value is 0.

";

%feature("docstring")  itk::simple::ResampleImageFilter::SetInterpolator "

Get/Set the interpolator function. The default is LinearInterpolateImageFunction <InputImageType, TInterpolatorPrecisionType>. Some other options are NearestNeighborInterpolateImageFunction (useful for binary masks and other images with a small number of
possible pixel values), and BSplineInterpolateImageFunction (which provides a higher order of interpolation).

";

%feature("docstring")  itk::simple::ResampleImageFilter::SetOutputDirection "

Set the output direciton cosine matrix.

";

%feature("docstring")  itk::simple::ResampleImageFilter::SetOutputOrigin "

Set the output image origin.

";

%feature("docstring")  itk::simple::ResampleImageFilter::SetOutputPixelType "

Set the output pixel type, if sitkUnknown then the input type is used.

";

%feature("docstring")  itk::simple::ResampleImageFilter::SetOutputSpacing "

Set the output image spacing.

";

%feature("docstring")  itk::simple::ResampleImageFilter::SetReferenceImage "

This methods sets the output size, origin, spacing and direction to
that of the provided image

";

%feature("docstring")  itk::simple::ResampleImageFilter::SetSize "

Get/Set the size of the output image.

";

%feature("docstring")  itk::simple::ResampleImageFilter::SetTransform "
";

%feature("docstring")  itk::simple::ResampleImageFilter::SetUseNearestNeighborExtrapolator "

Enables the nearest neighbor extrapolator as opposed to the constant
pixel value.

";

%feature("docstring")  itk::simple::ResampleImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ResampleImageFilter::UseNearestNeighborExtrapolatorOff "
";

%feature("docstring")  itk::simple::ResampleImageFilter::UseNearestNeighborExtrapolatorOn "

Set the value of UseNearestNeighborExtrapolator to true or false
respectfully.

";

%feature("docstring")  itk::simple::ResampleImageFilter::~ResampleImageFilter "

Destructor

";


%feature("docstring") itk::simple::RescaleIntensityImageFilter "

Applies a linear transformation to the intensity levels of the input Image .


RescaleIntensityImageFilter applies pixel-wise a linear transformation to the intensity values of
input image pixels. The linear transformation is defined by the user
in terms of the minimum and maximum values that the output image
should have.

The following equation gives the mapping of the intensity values


\\\\[ outputPixel = ( inputPixel - inputMin) \\\\cdot
\\\\frac{(outputMax - outputMin )}{(inputMax - inputMin)} + outputMin
\\\\]
 All computations are performed in the precision of the input pixel's
RealType. Before assigning the computed value to the output pixel.

NOTE: In this filter the minimum and maximum values of the input image
are computed internally using the MinimumMaximumImageCalculator . Users are not supposed to set those values in this filter. If you
need a filter where you can set the minimum and maximum values of the
input, please use the IntensityWindowingImageFilter . If you want a filter that can use a user-defined linear
transformation for the intensity, then please use the ShiftScaleImageFilter .


See:
 IntensityWindowingImageFilter

 itk::simple::RescaleIntensity for the procedural interface

 itk::RescaleIntensityImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRescaleIntensityImageFilter.h
";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::Execute "
";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::GetOutputMaximum "
";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::GetOutputMinimum "
";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::RescaleIntensityImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::SetOutputMaximum "
";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::SetOutputMinimum "
";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RescaleIntensityImageFilter::~RescaleIntensityImageFilter "

Destructor

";


%feature("docstring") itk::simple::RichardsonLucyDeconvolutionImageFilter "

Deconvolve an image using the Richardson-Lucy deconvolution algorithm.


This filter implements the Richardson-Lucy deconvolution algorithm as
defined in Bertero M and Boccacci P, \"Introduction to Inverse
Problems in Imaging\", 1998. The algorithm assumes that the input
image has been formed by a linear shift-invariant system with a known
kernel.

The Richardson-Lucy algorithm assumes that noise in the image follows
a Poisson distribution and that the distribution for each pixel is
independent of the other pixels.

This code was adapted from the Insight Journal contribution:

\"Deconvolution: infrastructure and reference algorithms\" by Gaetan
Lehmann https://hdl.handle.net/10380/3207


Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France

Cory Quammen, The University of North Carolina at Chapel Hill

See:
 IterativeDeconvolutionImageFilter

 LandweberDeconvolutionImageFilter

 ProjectedLandweberDeconvolutionImageFilter

 itk::simple::RichardsonLucyDeconvolution for the procedural interface

 itk::RichardsonLucyDeconvolutionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRichardsonLucyDeconvolutionImageFilter.h
";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::GetBoundaryCondition "
";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::GetNormalize "
";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::GetNumberOfIterations "

Get the number of iterations.

";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::GetOutputRegionMode "
";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::NormalizeOff "
";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::NormalizeOn "

Set the value of Normalize to true or false respectfully.

";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::RichardsonLucyDeconvolutionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::SetBoundaryCondition "
";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::SetNormalize "

Normalize the output image by the sum of the kernel components

";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::SetNumberOfIterations "

Set the number of iterations.

";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::SetOutputRegionMode "
";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RichardsonLucyDeconvolutionImageFilter::~RichardsonLucyDeconvolutionImageFilter "

Destructor

";


%feature("docstring") itk::simple::RoundImageFilter "

Rounds the value of each pixel.


The computations are performed using itk::Math::Round(x).
See:
 itk::simple::Round for the procedural interface

 itk::RoundImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkRoundImageFilter.h
";

%feature("docstring")  itk::simple::RoundImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::RoundImageFilter::Execute "
";

%feature("docstring")  itk::simple::RoundImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::RoundImageFilter::RoundImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::RoundImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::RoundImageFilter::~RoundImageFilter "

Destructor

";


%feature("docstring") itk::simple::SLICImageFilter "

Simple Linear Iterative Clustering (SLIC) super-pixel segmentation.


The Simple Linear Iterative Clustering (SLIC) algorithm groups pixels
into a set of labeled regions or super-pixels. Super-pixels follow
natural image boundaries, are compact, and are nearly uniform regions
which can be used as a larger primitive for more efficient
computation. The SLIC algorithm can be viewed as a spatially
constrained iterative k-means method.

The original algorithm was designed to cluster on the joint domain of
the images index space and it's CIELAB color space. This
implementation works with images of arbitrary dimension as well as
scalar, single channel, images and most multi-component image types
including ITK's arbitrary length VectorImage .

The distance between a pixel and a cluster is the sum of squares of
the difference between their joint range and domains ( index and value
). The computation is done in index space with scales provided by the
SpatialProximityWeight parameters.

The output is a label image with each label representing a superpixel
cluster. Every pixel in the output is labeled, and the starting label
id is zero.

This code was contributed in the Insight Journal paper: \"Scalable
Simple Linear Iterative Clustering (SSLIC) Using a Generic and
Parallel Approach\" by Lowekamp B. C., Chen D. T., Yaniv Z., Yoo T. S. https://hdl.handle.net/1926/3596
See:
 itk::simple::SLIC for the procedural interface

 itk::SLICImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSLICImageFilter.h
";

%feature("docstring")  itk::simple::SLICImageFilter::EnforceConnectivityOff "
";

%feature("docstring")  itk::simple::SLICImageFilter::EnforceConnectivityOn "

Set the value of EnforceConnectivity to true or false respectfully.

";

%feature("docstring")  itk::simple::SLICImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SLICImageFilter::GetAverageResidual "

Get the current average cluster residual. After each iteration the
residual is computed as the distance between the current clusters and
the previous. This is averaged so that the value is independent of the
number of clusters.


This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::SLICImageFilter::GetEnforceConnectivity "
";

%feature("docstring")  itk::simple::SLICImageFilter::GetInitializationPerturbation "
";

%feature("docstring")  itk::simple::SLICImageFilter::GetMaximumNumberOfIterations "
";

%feature("docstring")  itk::simple::SLICImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SLICImageFilter::GetSpatialProximityWeight "
";

%feature("docstring")  itk::simple::SLICImageFilter::GetSuperGridSize "
";

%feature("docstring")  itk::simple::SLICImageFilter::InitializationPerturbationOff "
";

%feature("docstring")  itk::simple::SLICImageFilter::InitializationPerturbationOn "

Set the value of InitializationPerturbation to true or false
respectfully.

";

%feature("docstring")  itk::simple::SLICImageFilter::SetEnforceConnectivity "

Post processing step to enforce superpixel morphology. Enable an
additional computation which ensures all label pixels of the same
value are spatially connected. Disconnected labeled components are
assigned a new value if of sufficient size, or are relabeled to the
previously encountered value if small.

";

%feature("docstring")  itk::simple::SLICImageFilter::SetInitializationPerturbation "

Enable perturbation of initial cluster center location. After grid
based initialization, this option enables moving the initial cluster
center location to the minimum gradient in a small neighborhood. If
the grid size is less than three this is automatically disabled.

";

%feature("docstring")  itk::simple::SLICImageFilter::SetMaximumNumberOfIterations "

Number of iterations to run. Specify the number of iterations to run
when optimizing the clusters.

";

%feature("docstring")  itk::simple::SLICImageFilter::SetSpatialProximityWeight "

The spatial weight for the distance function. Increasing this value
makes the superpixel shape more regular, but more varied in image
values. The range of the pixel values and image dimension can effect
the appropriate value.

";

%feature("docstring")  itk::simple::SLICImageFilter::SetSuperGridSize "
";

%feature("docstring")  itk::simple::SLICImageFilter::SLICImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SLICImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SLICImageFilter::~SLICImageFilter "

Destructor

";


%feature("docstring") itk::simple::STAPLEImageFilter "

The STAPLE filter implements the Simultaneous Truth and Performance
Level Estimation algorithm for generating ground truth volumes from a
set of binary expert segmentations.


The STAPLE algorithm treats segmentation as a pixelwise
classification, which leads to an averaging scheme that accounts for
systematic biases in the behavior of experts in order to generate a
fuzzy ground truth volume and simultaneous accuracy assessment of each
expert. The ground truth volumes produced by this filter are floating
point volumes of values between zero and one that indicate probability
of each pixel being in the object targeted by the segmentation.

The STAPLE algorithm is described in

S. Warfield, K. Zou, W. Wells, \"Validation of image segmentation and
expert  quality with an expectation-maximization algorithm\" in MICCAI
2002: Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag,
Heidelberg, Germany, 2002, pp. 298-306

INPUTS
Input volumes to the STAPLE filter must be binary segmentations of an
image, that is, there must be a single foreground value that
represents positively classified pixels (pixels that are considered to
belong inside the segmentation). Any number of background pixel values
may be present in the input images. You can, for example, input
volumes with many different labels as long as the structure you are
interested in creating ground truth for is consistently labeled among
all input volumes. Pixel type of the input volumes does not matter.
Specify the label value for positively classified pixels using
SetForegroundValue. All other labels will be considered to be
negatively classified pixels (background).
 Input volumes must all contain the same size RequestedRegions.

OUTPUTS
The STAPLE filter produces a single output volume with a range of
floating point values from zero to one. IT IS VERY IMPORTANT TO
INSTANTIATE THIS FILTER WITH A FLOATING POINT OUTPUT TYPE (floats or
doubles). You may threshold the output above some probability
threshold if you wish to produce a binary ground truth.
PARAMETERS
The STAPLE algorithm requires a number of inputs. You may specify any
number of input volumes using the SetInput(i, p_i) method, where i
ranges from zero to N-1, N is the total number of input segmentations,
and p_i is the SmartPointer to the i-th segmentation.
 The SetConfidenceWeight parameter is a modifier for the prior
probability that any pixel would be classified as inside the target
object. This implementation of the STAPLE algorithm automatically
calculates prior positive classification probability as the average
fraction of the image volume filled by the target object in each input
segmentation. The ConfidenceWeight parameter allows for scaling the of
this default prior probability: if g_t is the prior probability that a
pixel would be classified inside the target object, then g_t is set to
g_t * ConfidenceWeight before iterating on the solution. In general
ConfidenceWeight should be left to the default of 1.0.

You must provide a foreground value using SetForegroundValue that the
STAPLE algorithm will use to identify positively classified pixels in
the the input images. All other values in the image will be treated as
background values. For example, if your input segmentations consist of
1's everywhere inside the segmented region, then use
SetForegroundValue(1).

The STAPLE algorithm is an iterative E-M algorithm and will converge
on a solution after some number of iterations that cannot be known a
priori. After updating the filter, the total elapsed iterations taken
to converge on the solution can be queried through GetElapsedIterations() . You may also specify a MaximumNumberOfIterations, after which the
algorithm will stop iterating regardless of whether or not it has
converged. This implementation of the STAPLE algorithm will find the
solution to within seven digits of precision unless it is stopped
early.

Once updated, the Sensitivity (true positive fraction, q) and
Specificity (true negative fraction, q) for each expert input volume
can be queried using GetSensitivity(i) and GetSpecificity(i), where i
is the i-th input volume.

REQUIRED PARAMETERS
The only required parameters for this filter are the ForegroundValue
and the input volumes. All other parameters may be safely left to
their default values. Please see the paper cited above for more
information on the STAPLE algorithm and its parameters. A proper
understanding of the algorithm is important for interpreting the
results that it produces.
EVENTS
This filter invokes IterationEvent() at each iteration of the E-M
algorithm. Setting the AbortGenerateData() flag will cause the
algorithm to halt after the current iteration and produce results just
as if it had converged. The algorithm makes no attempt to report its
progress since the number of iterations needed cannot be known in
advance.

See:
 itk::simple::STAPLE for the procedural interface


C++ includes: sitkSTAPLEImageFilter.h
";

%feature("docstring")  itk::simple::STAPLEImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::STAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::STAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::STAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::STAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::STAPLEImageFilter::Execute "
";

%feature("docstring")  itk::simple::STAPLEImageFilter::GetConfidenceWeight "

Scales the estimated prior probability that a pixel will be inside the
targeted object of segmentation. The default prior probability g_t is
calculated automatically as the average fraction of positively
classified pixels to the total size of the volume (across all input
volumes). ConfidenceWeight will scale this default value as g_t = g_t
* ConfidenceWeight. In general, ConfidenceWeight should be left to the
default of 1.0.

";

%feature("docstring")  itk::simple::STAPLEImageFilter::GetElapsedIterations "

Get the number of elapsed iterations of the iterative E-M algorithm.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::STAPLEImageFilter::GetForegroundValue "

Set get the binary ON value of the input image.

";

%feature("docstring")  itk::simple::STAPLEImageFilter::GetMaximumIterations "

Set/Get the maximum number of iterations after which the STAPLE
algorithm will be considered to have converged. In general this SHOULD
NOT be set and the algorithm should be allowed to converge on its own.

";

%feature("docstring")  itk::simple::STAPLEImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::STAPLEImageFilter::GetSensitivity "

After the filter is updated, this method returns a std::vector<double>
of all Sensitivity (true positive fraction, p) values for the expert
input volumes.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::STAPLEImageFilter::GetSpecificity "

After the filter is updated, this method returns the Specificity (true
negative fraction, q) value for the i-th expert input volume.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::STAPLEImageFilter::SetConfidenceWeight "

Scales the estimated prior probability that a pixel will be inside the
targeted object of segmentation. The default prior probability g_t is
calculated automatically as the average fraction of positively
classified pixels to the total size of the volume (across all input
volumes). ConfidenceWeight will scale this default value as g_t = g_t
* ConfidenceWeight. In general, ConfidenceWeight should be left to the
default of 1.0.

";

%feature("docstring")  itk::simple::STAPLEImageFilter::SetForegroundValue "

Set get the binary ON value of the input image.

";

%feature("docstring")  itk::simple::STAPLEImageFilter::SetMaximumIterations "

Set/Get the maximum number of iterations after which the STAPLE
algorithm will be considered to have converged. In general this SHOULD
NOT be set and the algorithm should be allowed to converge on its own.

";

%feature("docstring")  itk::simple::STAPLEImageFilter::STAPLEImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::STAPLEImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::STAPLEImageFilter::~STAPLEImageFilter "

Destructor

";


%feature("docstring") itk::simple::SaltAndPepperNoiseImageFilter "

Alter an image with fixed value impulse noise, often called salt and
pepper noise.


Salt (sensor saturation) and pepper (dead pixels) noise is a special
kind of impulse noise where the value of the noise is either the
maximum possible value in the image or its minimum. This is not
necessarily the maximal/minimal possible intensity value based on the
pixel type. For example, the native pixel type for CT is a signed 16
bit integer, but only 12 bits used, so we would like to set the salt
and pepper values to match this smaller intensity range and not the
range the pixel type represents. It can be modeled as:


$ I = \\\\begin{cases} M, & \\\\quad \\\\text{if } U < p/2 \\\\\\\\ m,
& \\\\quad \\\\text{if } U > 1 - p/2 \\\\\\\\ I_0, & \\\\quad
\\\\text{if } p/2 \\\\geq U \\\\leq 1 - p/2 \\\\end{cases} $

where $ p $ is the probability of the noise event, $ U $ is a uniformly distributed random variable in the $ [0,1] $ range, $ M $ is the greatest possible pixel value, and $ m $ the smallest possible pixel value.
 Pixel alteration occurs at a user defined probability. Salt and
pepper pixels are equally distributed.


Gaetan Lehmann
 This code was contributed in the Insight Journal paper \"Noise
Simulation\". https://hdl.handle.net/10380/3158
See:
 itk::simple::SaltAndPepperNoise for the procedural interface

 itk::SaltAndPepperNoiseImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSaltAndPepperNoiseImageFilter.h
";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::Execute "
";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::GetProbability "

Set/Get the probability of the salt and pepper noise event. Defaults
to 0.01.

";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::GetSeed "
";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::SaltAndPepperNoiseImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::SetProbability "

Set/Get the probability of the salt and pepper noise event. Defaults
to 0.01.

";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::SetSeed "
";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SaltAndPepperNoiseImageFilter::~SaltAndPepperNoiseImageFilter "

Destructor

";


%feature("docstring") itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter "

Dense implementation of the Chan and Vese multiphase level set image
filter.


This code was adapted from the paper: \"An active contour model
without edges\" T. Chan and L. Vese. In Scale-Space Theories in
Computer Vision, pages 141-151, 1999.


Mosaliganti K., Smith B., Gelas A., Gouaillard A., Megason S.
 This code was taken from the Insight Journal paper: \"Cell Tracking
using Coupled Active Surfaces for Nuclei and Membranes\" http://www.insight-journal.org/browse/publication/642 https://hdl.handle.net/10380/3055

That is based on the papers: \"Level Set Segmentation: Active Contours
without edge\" http://www.insight-journal.org/browse/publication/322 https://hdl.handle.net/1926/1532

and

\"Level set segmentation using coupled active surfaces\" http://www.insight-journal.org/browse/publication/323 https://hdl.handle.net/1926/1533
See:
 itk::simple::ScalarChanAndVeseDenseLevelSet for the procedural interface

 itk::ScalarChanAndVeseDenseLevelSetImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkScalarChanAndVeseDenseLevelSetImageFilter.h
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetAreaWeight "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetCurvatureWeight "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetElapsedIterations "

Number of iterations run.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetEpsilon "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetHeavisideStepFunction "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetLambda1 "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetLambda2 "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetReinitializationSmoothingWeight "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetRMSChange "

The Root Mean Square of the levelset upon termination.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetUseImageSpacing "

Use the image spacing information in calculations. Use this option if
you want derivatives in physical space. Default is UseImageSpacingOn.

";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetVolume "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::GetVolumeMatchingWeight "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::ScalarChanAndVeseDenseLevelSetImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetAreaWeight "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetCurvatureWeight "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetEpsilon "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetHeavisideStepFunction "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetLambda1 "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetLambda2 "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetReinitializationSmoothingWeight "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetUseImageSpacing "

Use the image spacing information in calculations. Use this option if
you want derivatives in physical space. Default is UseImageSpacingOn.

";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetVolume "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::SetVolumeMatchingWeight "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::ScalarChanAndVeseDenseLevelSetImageFilter::~ScalarChanAndVeseDenseLevelSetImageFilter "

Destructor

";


%feature("docstring") itk::simple::ScalarConnectedComponentImageFilter "

A connected components filter that labels the objects in an arbitrary
image. Two pixels are similar if they are within threshold of each
other. Uses ConnectedComponentFunctorImageFilter .



See:
 itk::simple::ScalarConnectedComponent for the procedural interface

 itk::ScalarConnectedComponentImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkScalarConnectedComponentImageFilter.h
";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::Execute "
";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::GetDistanceThreshold "
";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::GetFullyConnected "
";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::ScalarConnectedComponentImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::SetDistanceThreshold "
";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::SetFullyConnected "
";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ScalarConnectedComponentImageFilter::~ScalarConnectedComponentImageFilter "

Destructor

";


%feature("docstring") itk::simple::ScalarImageKmeansImageFilter "

Classifies the intensity values of a scalar image using the K-Means
algorithm.


Given an input image with scalar values, it uses the K-Means
statistical classifier in order to define labels for every pixel in
the image. The filter is templated over the type of the input image.
The output image is predefined as having the same dimension of the
input image and pixel type unsigned char, under the assumption that
the classifier will generate less than 256 classes.

You may want to look also at the RelabelImageFilter that may be used
as a postprocessing stage, in particular if you are interested in
ordering the labels by their relative size in number of pixels.


See:
 Image

 ImageKmeansModelEstimator

 KdTreeBasedKmeansEstimator, WeightedCentroidKdTreeGenerator, KdTree

 RelabelImageFilter

 itk::simple::ScalarImageKmeans for the procedural interface

 itk::ScalarImageKmeansImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkScalarImageKmeansImageFilter.h
";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::GetClassWithInitialMean "
";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::GetFinalMeans "

Return the array of Means found after the classification.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::GetUseNonContiguousLabels "

Set/Get the UseNonContiguousLabels flag. When this is set to false the
labels are numbered contiguously, like in {0,1,3..N}. When the flag is
set to true, the labels are selected in order to span the dynamic
range of the output image. This last option is useful when the output
image is intended only for display. The default value is false.

";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::ScalarImageKmeansImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::SetClassWithInitialMean "
";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::SetUseNonContiguousLabels "

Set/Get the UseNonContiguousLabels flag. When this is set to false the
labels are numbered contiguously, like in {0,1,3..N}. When the flag is
set to true, the labels are selected in order to span the dynamic
range of the output image. This last option is useful when the output
image is intended only for display. The default value is false.

";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::UseNonContiguousLabelsOff "
";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::UseNonContiguousLabelsOn "

Set the value of UseNonContiguousLabels to true or false respectfully.

";

%feature("docstring")  itk::simple::ScalarImageKmeansImageFilter::~ScalarImageKmeansImageFilter "

Destructor

";


%feature("docstring") itk::simple::ScalarToRGBColormapImageFilter "

Implements pixel-wise intensity->rgb mapping operation on one image.


This class is parameterized over the type of the input image and the
type of the output image.

The input image's scalar pixel values are mapped into a color map. The
color map is specified by passing the SetColormap function one of the
predefined maps. The following selects the
\"RGBColormapFilterEnum::Hot\" colormap:

You can also specify a custom color map. This is done by creating a
CustomColormapFunction, and then creating lists of values for the red,
green, and blue channel. An example of setting the red channel of a
colormap with only 2 colors is given below. The blue and green
channels should be specified in the same manner.


The range of values present in the input image is the range that is
mapped to the entire range of colors.

This code was contributed in the Insight Journal paper: \"Meeting Andy
Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK\" by
Tustison N., Zhang H., Lehmann G., Yushkevich P., Gee J. https://hdl.handle.net/1926/1452 http://www.insight-journal.org/browse/publication/285


See:
 BinaryFunctionImageFilter TernaryFunctionImageFilter

 itk::simple::ScalarToRGBColormap for the procedural interface

 itk::ScalarToRGBColormapImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkScalarToRGBColormapImageFilter.h
";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::GetColormap "

Set/Get the colormap object.

";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::GetUseInputImageExtremaForScaling "

Set/Get UseInputImageExtremaForScaling. If true, the colormap uses the
min and max values from the image to scale appropriately. Otherwise,
these values can be set in the colormap manually.

";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::ScalarToRGBColormapImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::SetColormap "
";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::SetUseInputImageExtremaForScaling "

Set/Get UseInputImageExtremaForScaling. If true, the colormap uses the
min and max values from the image to scale appropriately. Otherwise,
these values can be set in the colormap manually.

";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::UseInputImageExtremaForScalingOff "
";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::UseInputImageExtremaForScalingOn "

Set the value of UseInputImageExtremaForScaling to true or false
respectfully.

";

%feature("docstring")  itk::simple::ScalarToRGBColormapImageFilter::~ScalarToRGBColormapImageFilter "

Destructor

";


%feature("docstring") itk::simple::ScaleSkewVersor3DTransform "

A over parameterized 3D Affine transform composed of the addition of a
versor rotation matrix, a scale matrix and a skew matrix around a
fixed center with translation.



See:
 itk::ScaleSkewVersor3DTransform


C++ includes: sitkScaleSkewVersor3DTransform.h
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::GetCenter "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::GetMatrix "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::GetScale "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::GetSkew "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::GetTranslation "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::GetVersor "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::ScaleSkewVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::ScaleSkewVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::ScaleSkewVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::ScaleSkewVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::ScaleSkewVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::SetRotation "

parameter

";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::SetRotation "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::SetScale "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::SetSkew "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::SetTranslation "
";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::Translate "

additional methods

";

%feature("docstring")  itk::simple::ScaleSkewVersor3DTransform::~ScaleSkewVersor3DTransform "
";


%feature("docstring") itk::simple::ScaleTransform "

A 2D or 3D anisotropic scale of coordinate space around a fixed
center.



See:
 itk::ScaleTransform


C++ includes: sitkScaleTransform.h
";

%feature("docstring")  itk::simple::ScaleTransform::GetCenter "
";

%feature("docstring")  itk::simple::ScaleTransform::GetMatrix "

additional methods

";

%feature("docstring")  itk::simple::ScaleTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ScaleTransform::GetScale "
";

%feature("docstring")  itk::simple::ScaleTransform::ScaleTransform "
";

%feature("docstring")  itk::simple::ScaleTransform::ScaleTransform "
";

%feature("docstring")  itk::simple::ScaleTransform::ScaleTransform "
";

%feature("docstring")  itk::simple::ScaleTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::ScaleTransform::SetScale "
";

%feature("docstring")  itk::simple::ScaleTransform::~ScaleTransform "
";


%feature("docstring") itk::simple::ScaleVersor3DTransform "

A parameterized 3D transform composed of the addition of a versor
rotation matrix and a scale matrix around a fixed center with
translation.



See:
 itk::ScaleVersor3DTransform


C++ includes: sitkScaleVersor3DTransform.h
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::GetCenter "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::GetMatrix "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::GetScale "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::GetTranslation "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::GetVersor "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::ScaleVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::ScaleVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::ScaleVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::ScaleVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::ScaleVersor3DTransform "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::SetRotation "

parameter

";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::SetRotation "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::SetScale "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::SetTranslation "
";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::Translate "

additional methods

";

%feature("docstring")  itk::simple::ScaleVersor3DTransform::~ScaleVersor3DTransform "
";


%feature("docstring") itk::simple::ShanbhagThresholdImageFilter "

Threshold an image using the Shanbhag Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the ShanbhagThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::ShanbhagThreshold for the procedural interface

 itk::ShanbhagThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkShanbhagThresholdImageFilter.h
";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::ShanbhagThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ShanbhagThresholdImageFilter::~ShanbhagThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::ShapeDetectionLevelSetImageFilter "

Segments structures in images based on a user supplied edge potential
map.


IMPORTANT
The SegmentationLevelSetImageFilter class and the ShapeDetectionLevelSetFunction class contain additional information necessary to gain full
understanding of how to use this filter.
OVERVIEW
This class is a level set method segmentation filter. An initial
contour is propagated outwards (or inwards) until it ''sticks'' to the
shape boundaries. This is done by using a level set speed function
based on a user supplied edge potential map. This approach for
segmentation follows that of Malladi et al (1995).
INPUTS
This filter requires two inputs. The first input is a initial level
set. The initial level set is a real image which contains the initial
contour/surface as the zero level set. For example, a signed distance
function from the initial contour/surface is typically used. Note that
for this algorithm the initial contour has to be wholly within (or
wholly outside) the structure to be segmented.

The second input is the feature image. For this filter, this is the
edge potential map. General characteristics of an edge potential map
is that it has values close to zero in regions near the edges and
values close to one inside the shape itself. Typically, the edge
potential map is compute from the image gradient, for example:
\\\\[ g(I) = 1 / ( 1 + | (\\\\nabla * G)(I)| ) \\\\] \\\\[ g(I) = \\\\exp^{-|(\\\\nabla * G)(I)|} \\\\]

where $ I $ is image intensity and $ (\\\\nabla * G) $ is the derivative of Gaussian operator.


See SegmentationLevelSetImageFilter and SparseFieldLevelSetImageFilter for more information on Inputs.
PARAMETERS
The PropagationScaling parameter can be used to switch from
propagation outwards (POSITIVE scaling parameter) versus propagating
inwards (NEGATIVE scaling parameter).
 The smoothness of the resulting contour/surface can be adjusted using
a combination of PropagationScaling and CurvatureScaling parameters.
The larger the CurvatureScaling parameter, the smoother the resulting
contour. The CurvatureScaling parameter should be non-negative for
proper operation of this algorithm. To follow the implementation in
Malladi et al paper, set the PropagtionScaling to $\\\\pm 1.0$ and CurvatureScaling to $ \\\\epsilon $ .

Note that there is no advection term for this filter. Setting the
advection scaling will have no effect.

OUTPUTS
The filter outputs a single, scalar, real-valued image. Negative
values in the output image represent the inside of the segmented
region and positive values in the image represent the outside of the
segmented region. The zero crossings of the image correspond to the
position of the propagating front.

See SparseFieldLevelSetImageFilter and SegmentationLevelSetImageFilter for more information.
REFERENCES

\"Shape Modeling with Front Propagation: A Level Set Approach\", R.
Malladi, J. A. Sethian and B. C. Vermuri. IEEE Trans. on Pattern
Analysis and Machine Intelligence, Vol 17, No. 2, pp 158-174, February
1995

See:
 SegmentationLevelSetImageFilter

 ShapeDetectionLevelSetFunction

 SparseFieldLevelSetImageFilter

 itk::simple::ShapeDetectionLevelSet for the procedural interface

 itk::ShapeDetectionLevelSetImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkShapeDetectionLevelSetImageFilter.h
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::Execute "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::GetCurvatureScaling "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::GetElapsedIterations "

Number of iterations run.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::GetPropagationScaling "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::GetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::GetRMSChange "

The Root Mean Square of the levelset upon termination.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::ReverseExpansionDirectionOff "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::ReverseExpansionDirectionOn "

Set the value of ReverseExpansionDirection to true or false
respectfully.

";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::SetCurvatureScaling "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::SetPropagationScaling "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::SetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::ShapeDetectionLevelSetImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ShapeDetectionLevelSetImageFilter::~ShapeDetectionLevelSetImageFilter "

Destructor

";


%feature("docstring") itk::simple::ShiftScaleImageFilter "

Shift and scale the pixels in an image.


ShiftScaleImageFilter shifts the input pixel by Shift (default 0.0) and then scales the
pixel by Scale (default 1.0). All computations are performed in the
precision of the input pixel's RealType. Before assigning the computed
value to the output pixel, the value is clamped at the NonpositiveMin
and max of the pixel type.
See:
 itk::simple::ShiftScale for the procedural interface

 itk::ShiftScaleImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkShiftScaleImageFilter.h
";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::GetOverflowCount "

Get the number of pixels that underflowed and overflowed.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::GetScale "

Set/Get the amount to Scale each Pixel. The Scale is applied after the
Shift.

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::GetShift "

Set/Get the amount to Shift each Pixel. The shift is followed by a
Scale.

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::GetUnderflowCount "

Get the number of pixels that underflowed and overflowed.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::SetScale "

Set/Get the amount to Scale each Pixel. The Scale is applied after the
Shift.

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::SetShift "

Set/Get the amount to Shift each Pixel. The shift is followed by a
Scale.

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::ShiftScaleImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ShiftScaleImageFilter::~ShiftScaleImageFilter "

Destructor

";


%feature("docstring") itk::simple::ShotNoiseImageFilter "

Alter an image with shot noise.


The shot noise follows a Poisson distribution:


$ I = N(I_0) $

where $ N(I_0) $ is a Poisson-distributed random variable of mean $ I_0 $ . The noise is thus dependent on the pixel intensities in the image.
 The intensities in the image can be scaled by a user provided value
to map pixel values to the actual number of particles. The scaling can
be seen as the inverse of the gain used during the acquisition. The
noisy signal is then scaled back to its input intensity range:


$ I = \\\\frac{N(I_0 \\\\times s)}{s} $

where $ s $ is the scale factor.
 The Poisson-distributed variable $ \\\\lambda $ is computed by using the algorithm:


$ \\\\begin{array}{l} k \\\\leftarrow 0 \\\\\\\\ p \\\\leftarrow 1
\\\\\\\\ \\\\textbf{repeat} \\\\\\\\ \\\\left\\\\{ \\\\begin{array}{l}
k \\\\leftarrow k+1 \\\\\\\\ p \\\\leftarrow p \\\\ast U()
\\\\end{array} \\\\right. \\\\\\\\ \\\\textbf{until } p >
e^{\\\\lambda} \\\\\\\\ \\\\textbf{return} (k) \\\\end{array} $

where $ U() $ provides a uniformly distributed random variable in the interval $ [0,1] $ .
 This algorithm is very inefficient for large values of $ \\\\lambda $ , though. Fortunately, the Poisson distribution can be accurately
approximated by a Gaussian distribution of mean and variance $ \\\\lambda $ when $ \\\\lambda $ is large enough. In this implementation, this value is considered to
be 50. This leads to the faster algorithm:


$ \\\\lambda + \\\\sqrt{\\\\lambda} \\\\times N()$

where $ N() $ is a normally distributed random variable of mean 0 and variance 1.

Gaetan Lehmann
 This code was contributed in the Insight Journal paper \"Noise
Simulation\". https://hdl.handle.net/10380/3158
See:
 itk::simple::ShotNoise for the procedural interface

 itk::ShotNoiseImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkShotNoiseImageFilter.h
";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::Execute "
";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::GetScale "

Set/Get the value to map the pixel value to the actual particle
counting. The scaling can be seen as the inverse of the gain used
during the acquisition. The noisy signal is then scaled back to its
input intensity range. Defaults to 1.0.

";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::GetSeed "
";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::SetScale "

Set/Get the value to map the pixel value to the actual particle
counting. The scaling can be seen as the inverse of the gain used
during the acquisition. The noisy signal is then scaled back to its
input intensity range. Defaults to 1.0.

";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::SetSeed "
";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::ShotNoiseImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ShotNoiseImageFilter::~ShotNoiseImageFilter "

Destructor

";


%feature("docstring") itk::simple::ShrinkImageFilter "

Reduce the size of an image by an integer factor in each dimension.


ShrinkImageFilter reduces the size of an image by an integer factor in each dimension.
The algorithm implemented is a simple subsample. The output image size
in each dimension is given by:

outputSize[j] = max( std::floor(inputSize[j]/shrinkFactor[j]), 1 );

NOTE: The physical centers of the input and output will be the same.
Because of this, the Origin of the output may not be the same as the
Origin of the input. Since this filter produces an image which is a
different resolution, origin and with different pixel spacing than its
input image, it needs to override several of the methods defined in ProcessObject in order to properly manage the pipeline execution model. In
particular, this filter overrides ProcessObject::GenerateInputRequestedRegion() and ProcessObject::GenerateOutputInformation() .

This filter is implemented as a multithreaded filter. It provides a
DynamicThreadedGenerateData() method for its implementation.
See:
 itk::simple::Shrink for the procedural interface

 itk::ShrinkImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkShrinkImageFilter.h
";

%feature("docstring")  itk::simple::ShrinkImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ShrinkImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ShrinkImageFilter::GetShrinkFactors "

Get the shrink factors.

";

%feature("docstring")  itk::simple::ShrinkImageFilter::SetShrinkFactor "

Custom public declarations

";

%feature("docstring")  itk::simple::ShrinkImageFilter::SetShrinkFactors "

Set the shrink factors. Values are clamped to a minimum value of 1.
Default is 1 for all dimensions.

";

%feature("docstring")  itk::simple::ShrinkImageFilter::ShrinkImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ShrinkImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ShrinkImageFilter::~ShrinkImageFilter "

Destructor

";


%feature("docstring") itk::simple::SigmoidImageFilter "

Computes the sigmoid function pixel-wise.


A linear transformation is applied first on the argument of the
sigmoid function. The resulting total transform is given by

\\\\[ f(x) = (Max-Min) \\\\cdot \\\\frac{1}{\\\\left(1+e^{- \\\\frac{
x - \\\\beta }{\\\\alpha}}\\\\right)} + Min \\\\]

Every output pixel is equal to f(x). Where x is the intensity of the
homologous input pixel, and alpha and beta are user-provided
constants.
See:
 itk::simple::Sigmoid for the procedural interface

 itk::SigmoidImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSigmoidImageFilter.h
";

%feature("docstring")  itk::simple::SigmoidImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SigmoidImageFilter::Execute "
";

%feature("docstring")  itk::simple::SigmoidImageFilter::GetAlpha "
";

%feature("docstring")  itk::simple::SigmoidImageFilter::GetBeta "
";

%feature("docstring")  itk::simple::SigmoidImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SigmoidImageFilter::GetOutputMaximum "
";

%feature("docstring")  itk::simple::SigmoidImageFilter::GetOutputMinimum "
";

%feature("docstring")  itk::simple::SigmoidImageFilter::SetAlpha "
";

%feature("docstring")  itk::simple::SigmoidImageFilter::SetBeta "
";

%feature("docstring")  itk::simple::SigmoidImageFilter::SetOutputMaximum "
";

%feature("docstring")  itk::simple::SigmoidImageFilter::SetOutputMinimum "
";

%feature("docstring")  itk::simple::SigmoidImageFilter::SigmoidImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SigmoidImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SigmoidImageFilter::~SigmoidImageFilter "

Destructor

";


%feature("docstring") itk::simple::SignedDanielssonDistanceMapImageFilter "

This class is parameterized over the type of the input image and the
type of the output image.

This filter computes the distance map of the input image as an
approximation with pixel accuracy to the Euclidean distance.

For purposes of evaluating the signed distance map, the input is
assumed to be binary composed of pixels with value 0 and non-zero.

The inside is considered as having negative distances. Outside is
treated as having positive distances. To change the convention, use
the InsideIsPositive(bool) function.

As a convention, the distance is evaluated from the boundary of the ON
pixels.

The filter returns


A signed distance map with the approximation to the euclidean
distance.

A voronoi partition. (See itkDanielssonDistanceMapImageFilter)

A vector map containing the component of the vector relating the
current pixel with the closest point of the closest object to this
pixel. Given that the components of the distance are computed in
\"pixels\", the vector is represented by an itk::Offset . That is, physical coordinates are not used. (See
itkDanielssonDistanceMapImageFilter)
 This filter internally uses the DanielssonDistanceMap filter. This
filter is N-dimensional.


See:
 itkDanielssonDistanceMapImageFilter

 itk::simple::SignedDanielssonDistanceMap for the procedural interface

 itk::SignedDanielssonDistanceMapImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSignedDanielssonDistanceMapImageFilter.h
";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::GetInsideIsPositive "

Get if the inside represents positive values in the signed distance
map. See GetInsideIsPositive()

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::GetSquaredDistance "

Get the distance squared.

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::GetUseImageSpacing "

Get whether spacing is used.

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::GetVoronoiMap "

Get Voronoi Map This map shows for each pixel what object is closest
to it. Each object should be labeled by a number (larger than 0), so
the map has a value for each pixel corresponding to the label of the
closest object.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::InsideIsPositiveOff "
";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::InsideIsPositiveOn "

Set the value of InsideIsPositive to true or false respectfully.

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::SetInsideIsPositive "

Set if the inside represents positive values in the signed distance
map. By convention ON pixels are treated as inside pixels.

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::SetSquaredDistance "

Set if the distance should be squared.

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::SetUseImageSpacing "

Set if image spacing should be used in computing distances.

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::SignedDanielssonDistanceMapImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::SquaredDistanceOff "
";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::SquaredDistanceOn "

Set the value of SquaredDistance to true or false respectfully.

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::SignedDanielssonDistanceMapImageFilter::~SignedDanielssonDistanceMapImageFilter "

Destructor

";


%feature("docstring") itk::simple::SignedMaurerDistanceMapImageFilter "

This filter calculates the Euclidean distance transform of a binary
image in linear time for arbitrary dimensions.


Inputs and Outputs
This is an image-to-image filter. The dimensionality is arbitrary. The
only dimensionality constraint is that the input and output images be
of the same dimensions and size. To maintain integer arithmetic within
the filter, the default output is the signed squared distance. This
implies that the input image should be of type \"unsigned int\" or
\"int\" whereas the output image is of type \"int\". Obviously, if the
user wishes to utilize the image spacing or to have a filter with the
Euclidean distance (as opposed to the squared distance), output image
types of float or double should be used.
 The inside is considered as having negative distances. Outside is
treated as having positive distances. To change the convention, use
the InsideIsPositive(bool) function.

Parameters
Set/GetBackgroundValue specifies the background of the value of the
input binary image. Normally this is zero and, as such, zero is the
default value. Other than that, the usage is completely analogous to
the itk::DanielssonDistanceImageFilter class except it does not return
the Voronoi map.
 Reference: C. R. Maurer, Jr., R. Qi, and V. Raghavan, \"A Linear Time
Algorithm for Computing Exact Euclidean Distance Transforms of Binary
Images in Arbitrary Dimensions\", IEEE - Transactions on Pattern
Analysis and Machine Intelligence, 25(2): 265-270, 2003.
See:
 itk::simple::SignedMaurerDistanceMap for the procedural interface

 itk::SignedMaurerDistanceMapImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSignedMaurerDistanceMapImageFilter.h
";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::GetBackgroundValue "

Set the background value which defines the object. Usually this value
is = 0.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::GetInsideIsPositive "

Get if the inside represents positive values in the signed distance
map.
See:
 GetInsideIsPositive()


";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::GetSquaredDistance "

Get the distance squared.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::GetUseImageSpacing "

Get whether spacing is used.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::InsideIsPositiveOff "
";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::InsideIsPositiveOn "

Set the value of InsideIsPositive to true or false respectfully.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::SetBackgroundValue "

Set the background value which defines the object. Usually this value
is = 0.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::SetInsideIsPositive "

Set if the inside represents positive values in the signed distance
map. By convention ON pixels are treated as inside pixels.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::SetSquaredDistance "

Set if the distance should be squared.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::SetUseImageSpacing "

Set if image spacing should be used in computing distances.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::SignedMaurerDistanceMapImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::SquaredDistanceOff "
";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::SquaredDistanceOn "

Set the value of SquaredDistance to true or false respectfully.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::SignedMaurerDistanceMapImageFilter::~SignedMaurerDistanceMapImageFilter "

Destructor

";


%feature("docstring") itk::simple::Similarity2DTransform "

A similarity 2D transform with rotation in radians and isotropic
scaling around a fixed center with translation.



See:
 itk::Similarity2DTransform


C++ includes: sitkSimilarity2DTransform.h
";

%feature("docstring")  itk::simple::Similarity2DTransform::GetAngle "
";

%feature("docstring")  itk::simple::Similarity2DTransform::GetCenter "
";

%feature("docstring")  itk::simple::Similarity2DTransform::GetMatrix "

additional methods

";

%feature("docstring")  itk::simple::Similarity2DTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::Similarity2DTransform::GetScale "
";

%feature("docstring")  itk::simple::Similarity2DTransform::GetTranslation "
";

%feature("docstring")  itk::simple::Similarity2DTransform::SetAngle "

parameter

";

%feature("docstring")  itk::simple::Similarity2DTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::Similarity2DTransform::SetMatrix "
";

%feature("docstring")  itk::simple::Similarity2DTransform::SetScale "
";

%feature("docstring")  itk::simple::Similarity2DTransform::SetTranslation "
";

%feature("docstring")  itk::simple::Similarity2DTransform::Similarity2DTransform "
";

%feature("docstring")  itk::simple::Similarity2DTransform::Similarity2DTransform "
";

%feature("docstring")  itk::simple::Similarity2DTransform::Similarity2DTransform "
";

%feature("docstring")  itk::simple::Similarity2DTransform::Similarity2DTransform "
";

%feature("docstring")  itk::simple::Similarity2DTransform::~Similarity2DTransform "
";


%feature("docstring") itk::simple::Similarity3DTransform "

A similarity 3D transform with rotation as a versor, and isotropic
scaling around a fixed center with translation.



See:
 itk::Similarity3DTransform


C++ includes: sitkSimilarity3DTransform.h
";

%feature("docstring")  itk::simple::Similarity3DTransform::GetCenter "
";

%feature("docstring")  itk::simple::Similarity3DTransform::GetMatrix "
";

%feature("docstring")  itk::simple::Similarity3DTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::Similarity3DTransform::GetScale "
";

%feature("docstring")  itk::simple::Similarity3DTransform::GetTranslation "
";

%feature("docstring")  itk::simple::Similarity3DTransform::GetVersor "
";

%feature("docstring")  itk::simple::Similarity3DTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::Similarity3DTransform::SetMatrix "
";

%feature("docstring")  itk::simple::Similarity3DTransform::SetRotation "

parameter

";

%feature("docstring")  itk::simple::Similarity3DTransform::SetRotation "
";

%feature("docstring")  itk::simple::Similarity3DTransform::SetScale "
";

%feature("docstring")  itk::simple::Similarity3DTransform::SetTranslation "
";

%feature("docstring")  itk::simple::Similarity3DTransform::Similarity3DTransform "
";

%feature("docstring")  itk::simple::Similarity3DTransform::Similarity3DTransform "
";

%feature("docstring")  itk::simple::Similarity3DTransform::Similarity3DTransform "
";

%feature("docstring")  itk::simple::Similarity3DTransform::Similarity3DTransform "
";

%feature("docstring")  itk::simple::Similarity3DTransform::Similarity3DTransform "
";

%feature("docstring")  itk::simple::Similarity3DTransform::Translate "

additional methods

";

%feature("docstring")  itk::simple::Similarity3DTransform::~Similarity3DTransform "
";


%feature("docstring") itk::simple::SimilarityIndexImageFilter "

Measures the similarity between the set of non-zero pixels of two
images.


SimilarityIndexImageFilter measures the similarity between the set non-zero pixels of two images
using the following formula: \\\\[ S = \\\\frac{2 | A \\\\cap B |}{|A| + |B|} \\\\] where $A$ and $B$ are respectively the set of non-zero pixels in the first and second
input images. Operator $|\\\\cdot|$ represents the size of a set and $\\\\cap$ represents the intersection of two sets.

The measure is derived from a reliability measure known as the kappa
statistic. $S$ is sensitive to both differences in size and in location and have
been in the literature for comparing two segmentation masks. For more
information see: \"Morphometric Analysis of White Matter Lesions in MR
Images: Method and  Validation\", A. P. Zijdenbos, B. M. Dawant, R. A.
Margolin and A. C. Palmer, IEEE Trans. on Medical Imaging, 13(4) pp
716-724,1994

This filter requires the largest possible region of the first image
and the same corresponding region in the second image. It behaves as
filter with two input and one output. Thus it can be inserted in a
pipeline with other filters. The filter passes the first input through
unmodified.

This filter is templated over the two input image type. It assume both
image have the same number of dimensions.


See:
 itk::SimilarityIndexImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSimilarityIndexImageFilter.h
";

%feature("docstring")  itk::simple::SimilarityIndexImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::SimilarityIndexImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SimilarityIndexImageFilter::GetSimilarityIndex "

Return the computed similarity index.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::SimilarityIndexImageFilter::SimilarityIndexImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SimilarityIndexImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SimilarityIndexImageFilter::~SimilarityIndexImageFilter "

Destructor

";


%feature("docstring") itk::simple::SimpleContourExtractorImageFilter "

Computes an image of contours which will be the contour of the first
image.


A pixel of the source image is considered to belong to the contour if
its pixel value is equal to the input foreground value and it has in
its neighborhood at least one pixel which its pixel value is equal to
the input background value. The output image will have pixels which
will be set to the output foreground value if they belong to the
contour, otherwise they will be set to the output background value.

The neighborhood \"radius\" is set thanks to the radius params.
See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::SimpleContourExtractor for the procedural interface

 itk::SimpleContourExtractorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSimpleContourExtractorImageFilter.h
";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::GetInputBackgroundValue "

Get the background value used in order to identify a background pixel
in the input image.

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::GetInputForegroundValue "

Get the foreground value used in order to identify a foreground pixel
in the input image.

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::GetOutputBackgroundValue "

Get the background value used in order to identify a background pixel
in the output image.

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::GetOutputForegroundValue "

Get the foreground value used in order to identify a foreground pixel
in the output image.

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::SetInputBackgroundValue "

Set the background value used in order to identify a background pixel
in the input image.

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::SetInputForegroundValue "

Set the foreground value used in order to identify a foreground pixel
in the input image.

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::SetOutputBackgroundValue "

Set the background value used in order to identify a background pixel
in the output image.

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::SetOutputForegroundValue "

Set the foreground value used in order to identify a foreground pixel
in the output image.

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::SimpleContourExtractorImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SimpleContourExtractorImageFilter::~SimpleContourExtractorImageFilter "

Destructor

";


%feature("docstring") itk::simple::SinImageFilter "

Computes the sine of each pixel.


The computations are performed using std::sin(x).
See:
 itk::simple::Sin for the procedural interface

 itk::SinImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSinImageFilter.h
";

%feature("docstring")  itk::simple::SinImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SinImageFilter::Execute "
";

%feature("docstring")  itk::simple::SinImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SinImageFilter::SinImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SinImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SinImageFilter::~SinImageFilter "

Destructor

";


%feature("docstring") itk::simple::SliceImageFilter "

Slices an image based on a starting index and a stopping index, and a
step size.


This class is designed to facilitate the implementation of extended
sliced based indexing into images.

The input and output image must be of the same dimension.

The input parameters are a starting and stopping index as well as a
stepping size. The starting index indicates the first pixels to be
used and for each dimension the index is incremented by the step until
the index is equal to or \"beyond\" the stopping index. If the step is
negative then the image will be reversed in the dimension, and the
stopping index is expected to be less then the starting index. If the
stopping index is already beyond the starting index then an image of
size zero will be returned.

The output image's starting index is always zero. The origin is the
physical location of the starting index. The output directions cosine
matrix is that of the input but with sign changes matching that of the
step's sign.


In certain combinations such as with start=1, and step>1 while the
physical location of the center of the pixel remains the same, the
extent (edge to edge space) of the output image will be beyond the
extent of the original image.

See:
 itk::simple::Slice for the procedural interface

 itk::SliceImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSliceImageFilter.h
";

%feature("docstring")  itk::simple::SliceImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SliceImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SliceImageFilter::GetStart "

Set/Get the first index extracted from the input image

";

%feature("docstring")  itk::simple::SliceImageFilter::GetStep "

Set/Get the stride of indexes extracted An exception will be generated
if 0.

";

%feature("docstring")  itk::simple::SliceImageFilter::GetStop "

Set/Get the excluded end of the range

";

%feature("docstring")  itk::simple::SliceImageFilter::SetStart "

Set/Get the first index extracted from the input image

";

%feature("docstring")  itk::simple::SliceImageFilter::SetStep "

Set/Get the stride of indexes extracted An exception will be generated
if 0.

";

%feature("docstring")  itk::simple::SliceImageFilter::SetStep "

Set the values of the Step vector all to value

";

%feature("docstring")  itk::simple::SliceImageFilter::SetStop "

Set/Get the excluded end of the range

";

%feature("docstring")  itk::simple::SliceImageFilter::SliceImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SliceImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SliceImageFilter::~SliceImageFilter "

Destructor

";


%feature("docstring") itk::simple::SmoothingRecursiveGaussianImageFilter "

Computes the smoothing of an image by convolution with the Gaussian
kernels implemented as IIR filters.


This filter is implemented using the recursive gaussian filters. For
multi-component images, the filter works on each component
independently.

For this filter to be able to run in-place the input and output image
types need to be the same and/or the same type as the RealImageType.
See:
 itk::simple::SmoothingRecursiveGaussian for the procedural interface

 itk::SmoothingRecursiveGaussianImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSmoothingRecursiveGaussianImageFilter.h
";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::Execute "
";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::GetNormalizeAcrossScale "

Set/Get the flag for normalizing the Gaussian over scale-space. This
method does not effect the output of this filter.


See:
 RecursiveGaussianImageFilter::SetNormalizeAcrossScale


";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::GetSigma "

Get the Sigma scalar. If the Sigma is anisotropic, we will just return
the Sigma along the first dimension.

";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::NormalizeAcrossScaleOff "
";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::NormalizeAcrossScaleOn "

Set the value of NormalizeAcrossScale to true or false respectfully.

";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::SetNormalizeAcrossScale "

Set/Get the flag for normalizing the Gaussian over scale-space. This
method does not effect the output of this filter.


See:
 RecursiveGaussianImageFilter::SetNormalizeAcrossScale


";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::SetSigma "

Set the standard deviation of the Gaussian used for smoothing. Sigma
is measured in the units of image spacing. You may use the method
SetSigma to set the same value across each axis or use the method
SetSigmaArray if you need different values along each axis.

";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::SetSigma "

Set the values of the Sigma vector all to value

";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::SmoothingRecursiveGaussianImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussianImageFilter::~SmoothingRecursiveGaussianImageFilter "

Destructor

";


%feature("docstring") itk::simple::SobelEdgeDetectionImageFilter "

A 2D or 3D edge detection using the Sobel operator.


This filter uses the Sobel operator to calculate the image gradient
and then finds the magnitude of this gradient vector. The Sobel
gradient magnitude (square-root sum of squares) is an indication of
edge strength.


See:
 ImageToImageFilter

 SobelOperator

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::SobelEdgeDetection for the procedural interface

 itk::SobelEdgeDetectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSobelEdgeDetectionImageFilter.h
";

%feature("docstring")  itk::simple::SobelEdgeDetectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SobelEdgeDetectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SobelEdgeDetectionImageFilter::SobelEdgeDetectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SobelEdgeDetectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SobelEdgeDetectionImageFilter::~SobelEdgeDetectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::SpeckleNoiseImageFilter "

Alter an image with speckle (multiplicative) noise.


The speckle noise follows a gamma distribution of mean 1 and standard
deviation provided by the user. The noise is proportional to the pixel
intensity.

It can be modeled as:


$ I = I_0 \\\\ast G $

where $ G $ is a is a gamma distributed random variable of mean 1 and variance
proportional to the noise level:

$ G \\\\sim \\\\Gamma(\\\\frac{1}{\\\\sigma^2}, \\\\sigma^2) $

Gaetan Lehmann
 This code was contributed in the Insight Journal paper \"Noise
Simulation\". https://hdl.handle.net/10380/3158
See:
 itk::simple::SpeckleNoise for the procedural interface

 itk::SpeckleNoiseImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSpeckleNoiseImageFilter.h
";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::Execute "
";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::GetSeed "
";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::GetStandardDeviation "

Set/Get the standard deviation of the gamma distribution. Defaults to
1.0.

";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::SetSeed "
";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::SetStandardDeviation "

Set/Get the standard deviation of the gamma distribution. Defaults to
1.0.

";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::SpeckleNoiseImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SpeckleNoiseImageFilter::~SpeckleNoiseImageFilter "

Destructor

";


%feature("docstring") itk::simple::SqrtImageFilter "

Computes the square root of each pixel.


The computations are performed using std::sqrt(x).
See:
 itk::simple::Sqrt for the procedural interface

 itk::SqrtImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSqrtImageFilter.h
";

%feature("docstring")  itk::simple::SqrtImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SqrtImageFilter::Execute "
";

%feature("docstring")  itk::simple::SqrtImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SqrtImageFilter::SqrtImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SqrtImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SqrtImageFilter::~SqrtImageFilter "

Destructor

";


%feature("docstring") itk::simple::SquareImageFilter "

Computes the square of the intensity values pixel-wise.



See:
 itk::simple::Square for the procedural interface

 itk::SquareImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSquareImageFilter.h
";

%feature("docstring")  itk::simple::SquareImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SquareImageFilter::Execute "
";

%feature("docstring")  itk::simple::SquareImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SquareImageFilter::SquareImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SquareImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SquareImageFilter::~SquareImageFilter "

Destructor

";


%feature("docstring") itk::simple::SquaredDifferenceImageFilter "

Implements pixel-wise the computation of squared difference.


This filter is parameterized over the types of the two input images
and the type of the output image.

Numeric conversions (castings) are done by the C++ defaults.

The filter will walk over all the pixels in the two input images, and
for each one of them it will do the following:


cast the input 1 pixel value to double

cast the input 2 pixel value to double

compute the difference of the two pixel values

compute the square of the difference

cast the double value resulting from sqr() to the pixel type of the
output image

store the casted value into the output image.
 The filter expect all images to have the same dimension (e.g. all 2D,
or all 3D, or all ND)
See:
 itk::simple::SquaredDifference for the procedural interface

 itk::SquaredDifferenceImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSquaredDifferenceImageFilter.h
";

%feature("docstring")  itk::simple::SquaredDifferenceImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::SquaredDifferenceImageFilter::Execute "
";

%feature("docstring")  itk::simple::SquaredDifferenceImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::SquaredDifferenceImageFilter::Execute "
";

%feature("docstring")  itk::simple::SquaredDifferenceImageFilter::Execute "
";

%feature("docstring")  itk::simple::SquaredDifferenceImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SquaredDifferenceImageFilter::SquaredDifferenceImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SquaredDifferenceImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SquaredDifferenceImageFilter::~SquaredDifferenceImageFilter "

Destructor

";


%feature("docstring") itk::simple::StandardDeviationProjectionImageFilter "

Mean projection.


This class was contributed to the Insight Journal by Gaetan Lehmann.
The original paper can be found at https://hdl.handle.net/1926/164


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ProjectionImageFilter

 MedianProjectionImageFilter

 MeanProjectionImageFilter

 SumProjectionImageFilter

 MeanProjectionImageFilter

 MaximumProjectionImageFilter

 MinimumProjectionImageFilter

 BinaryProjectionImageFilter

 itk::simple::StandardDeviationProjection for the procedural interface

 itk::StandardDeviationProjectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkStandardDeviationProjectionImageFilter.h
";

%feature("docstring")  itk::simple::StandardDeviationProjectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::StandardDeviationProjectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::StandardDeviationProjectionImageFilter::GetProjectionDimension "
";

%feature("docstring")  itk::simple::StandardDeviationProjectionImageFilter::SetProjectionDimension "
";

%feature("docstring")  itk::simple::StandardDeviationProjectionImageFilter::StandardDeviationProjectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::StandardDeviationProjectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::StandardDeviationProjectionImageFilter::~StandardDeviationProjectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::StatisticsImageFilter "

Compute min, max, variance and mean of an Image .


StatisticsImageFilter computes the minimum, maximum, sum, sum of squares, mean, variance
sigma of an image. The filter needs all of its input image. It behaves
as a filter with an input and output. Thus it can be inserted in a
pipline with other filters and the statistics will only be recomputed
if a downstream filter changes.

This filter is automatically multi-threaded and can stream its input
when NumberOfStreamDivisions is set to more than one. Statistics are independently computed for each streamed and threaded region then
merged.

Internally a compensated summation algorithm is used for the
accumulation of intensities to improve accuracy for large images.


See:
 itk::StatisticsImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkStatisticsImageFilter.h
";

%feature("docstring")  itk::simple::StatisticsImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::StatisticsImageFilter::GetMaximum "

Return the computed Maximum.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::StatisticsImageFilter::GetMean "

Return the computed Mean.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::StatisticsImageFilter::GetMinimum "

Return the computed Minimum.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::StatisticsImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::StatisticsImageFilter::GetSigma "

Return the computed Standard Deviation.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::StatisticsImageFilter::GetSum "

Return the compute Sum.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::StatisticsImageFilter::GetVariance "

Return the computed Variance.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::StatisticsImageFilter::StatisticsImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::StatisticsImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::StatisticsImageFilter::~StatisticsImageFilter "

Destructor

";


%feature("docstring") itk::simple::StochasticFractalDimensionImageFilter "

This filter computes the stochastic fractal dimension of the input
image.


The methodology is based on Madelbrot's fractal theory and the concept
of fractional Brownian motion and yields images which have been used
for classification and edge enhancement.

This class which is templated over the input and output images as well
as a mask image type. The input is a scalar image, an optional
neighborhood radius (default = 2), and an optional mask. The mask can
be specified to decrease computation time since, as the authors point
out, calculation is time-consuming.

This filter was contributed by Nick Tustison and James Gee from the
PICSL lab, at the University of Pennsylvania as an paper to the
Insight Journal:

\"Stochastic Fractal Dimension Image\" https://hdl.handle.net/1926/1525 http://www.insight-journal.org/browse/publication/318


Nick Tustison

See:
 itk::simple::StochasticFractalDimension for the procedural interface

 itk::StochasticFractalDimensionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkStochasticFractalDimensionImageFilter.h
";

%feature("docstring")  itk::simple::StochasticFractalDimensionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::StochasticFractalDimensionImageFilter::Execute "
";

%feature("docstring")  itk::simple::StochasticFractalDimensionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::StochasticFractalDimensionImageFilter::GetNeighborhoodRadius "

Manhattan radius used for evaluating the fractal dimension.

";

%feature("docstring")  itk::simple::StochasticFractalDimensionImageFilter::SetNeighborhoodRadius "

Manhattan radius used for evaluating the fractal dimension.

";

%feature("docstring")  itk::simple::StochasticFractalDimensionImageFilter::SetNeighborhoodRadius "

Set the values of the NeighborhoodRadius vector all to value

";

%feature("docstring")  itk::simple::StochasticFractalDimensionImageFilter::StochasticFractalDimensionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::StochasticFractalDimensionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::StochasticFractalDimensionImageFilter::~StochasticFractalDimensionImageFilter "

Destructor

";


%feature("docstring") itk::simple::SubtractImageFilter "

Pixel-wise subtraction of two images.


Subtract each pixel from image2 from its corresponding pixel in
image1:


This is done using


This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.

Additionally, a constant can be subtracted from every pixel in an
image using:



The result of AddImageFilter with a negative constant is not necessarily the same as SubtractImageFilter . This would be the case when the PixelType defines an operator-() that is not the inverse of operator+()

See:
 itk::simple::Subtract for the procedural interface

 itk::SubtractImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSubtractImageFilter.h
";

%feature("docstring")  itk::simple::SubtractImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::SubtractImageFilter::Execute "
";

%feature("docstring")  itk::simple::SubtractImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::SubtractImageFilter::Execute "
";

%feature("docstring")  itk::simple::SubtractImageFilter::Execute "
";

%feature("docstring")  itk::simple::SubtractImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SubtractImageFilter::SubtractImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SubtractImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SubtractImageFilter::~SubtractImageFilter "

Destructor

";


%feature("docstring") itk::simple::SumProjectionImageFilter "

Sum projection.


This class was contributed to the Insight Journal by Gaetan Lehmann.
The original paper can be found at https://hdl.handle.net/1926/164


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 ProjectionImageFilter

 MedianProjectionImageFilter

 MeanProjectionImageFilter

 MeanProjectionImageFilter

 MaximumProjectionImageFilter

 MinimumProjectionImageFilter

 BinaryProjectionImageFilter

 StandardDeviationProjectionImageFilter

 itk::simple::SumProjection for the procedural interface

 itk::SumProjectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkSumProjectionImageFilter.h
";

%feature("docstring")  itk::simple::SumProjectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SumProjectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SumProjectionImageFilter::GetProjectionDimension "
";

%feature("docstring")  itk::simple::SumProjectionImageFilter::SetProjectionDimension "
";

%feature("docstring")  itk::simple::SumProjectionImageFilter::SumProjectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SumProjectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SumProjectionImageFilter::~SumProjectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::SymmetricForcesDemonsRegistrationFilter "

Deformably register two images using the demons algorithm.


This class was contributed by Corinne Mattmann, ETH Zurich,
Switzerland. based on a variation of the DemonsRegistrationFilter . The basic modification is to use equation (5) from Thirion's paper
along with the modification for avoiding large deformations when
gradients have small values.

SymmetricForcesDemonsRegistrationFilter implements the demons deformable algorithm that register two images
by computing the deformation field which will map a moving image onto
a fixed image.

A deformation field is represented as a image whose pixel type is some
vector type with at least N elements, where N is the dimension of the
fixed image. The vector type must support element access via operator
[]. It is assumed that the vector elements behave like floating point
scalars.

This class is templated over the fixed image type, moving image type
and the deformation field type.

The input fixed and moving images are set via methods SetFixedImage
and SetMovingImage respectively. An initial deformation field maybe
set via SetInitialDisplacementField or SetInput. If no initial field
is set, a zero field is used as the initial condition.

The algorithm has one parameters: the number of iteration to be
performed.

The output deformation field can be obtained via methods GetOutput or
GetDisplacementField.

This class make use of the finite difference solver hierarchy. Update
for each iteration is computed in DemonsRegistrationFunction .


WARNING:
This filter assumes that the fixed image type, moving image type and
deformation field type all have the same number of dimensions.

See:
 SymmetricForcesDemonsRegistrationFunction

 DemonsRegistrationFilter

 DemonsRegistrationFunction

 itk::SymmetricForcesDemonsRegistrationFilter for the Doxygen on the original ITK class.


C++ includes: sitkSymmetricForcesDemonsRegistrationFilter.h
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::Execute "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetElapsedIterations "

Number of iterations run.


This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetIntensityDifferenceThreshold "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetMetric "

Get the metric value. The metric value is the mean square difference
in intensity between the fixed image and transforming moving image
computed over the the overlapping region between the two images. This
value is calculated for the current iteration

This is an active measurement. It may be accessed while the filter is
being executing in command call-backs and can be accessed after
execution.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetRMSChange "

Set/Get the root mean squared change of the previous iteration. May
not be used by all solvers.

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::GetUseImageSpacing "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetIntensityDifferenceThreshold "

Set/Get the threshold below which the absolute difference of intensity
yields a match. When the intensities match between a moving and fixed
image pixel, the update vector (for that iteration) will be the zero
vector. Default is 0.001.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetMaximumError "

Set/Get the desired maximum error of the Guassian kernel approximate.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetMaximumKernelWidth "

Set/Get the desired limits of the Gaussian kernel width.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetSmoothDisplacementField "

Set/Get whether the displacement field is smoothed (regularized).
Smoothing the displacement yields a solution elastic in nature. If
SmoothDisplacementField is on, then the displacement field is smoothed
with a Gaussian whose standard deviations are specified with SetStandardDeviations()

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetSmoothUpdateField "

Set/Get whether the update field is smoothed (regularized). Smoothing
the update field yields a solution viscous in nature. If
SmoothUpdateField is on, then the update field is smoothed with a
Gaussian whose standard deviations are specified with SetUpdateFieldStandardDeviations()

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetStandardDeviations "

Set/Get the Gaussian smoothing standard deviations for the
displacement field. The values are set with respect to pixel
coordinates.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetStandardDeviations "

Set the values of the StandardDeviations vector all to value

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the Gaussian smoothing standard deviations for the update field.
The values are set with respect to pixel coordinates.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetUpdateFieldStandardDeviations "

Set the values of the UpdateFieldStandardDeviations vector all to
value

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SetUseImageSpacing "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SmoothDisplacementFieldOff "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SmoothDisplacementFieldOn "

Set the value of SmoothDisplacementField to true or false
respectfully.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SmoothUpdateFieldOff "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SmoothUpdateFieldOn "

Set the value of SmoothUpdateField to true or false respectfully.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::SymmetricForcesDemonsRegistrationFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::UseImageSpacingOff "
";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::UseImageSpacingOn "

Set the value of UseImageSpacing to true or false respectfully.

";

%feature("docstring")  itk::simple::SymmetricForcesDemonsRegistrationFilter::~SymmetricForcesDemonsRegistrationFilter "

Destructor

";


%feature("docstring") itk::simple::TanImageFilter "

Computes the tangent of each input pixel.


The computations are performed using std::tan(x).
See:
 itk::simple::Tan for the procedural interface

 itk::TanImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkTanImageFilter.h
";

%feature("docstring")  itk::simple::TanImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::TanImageFilter::Execute "
";

%feature("docstring")  itk::simple::TanImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TanImageFilter::TanImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::TanImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::TanImageFilter::~TanImageFilter "

Destructor

";


%feature("docstring") itk::simple::TernaryAddImageFilter "

Pixel-wise addition of three images.


This class is templated over the types of the three input images and
the type of the output image. Numeric conversions (castings) are done
by the C++ defaults.
See:
 itk::simple::TernaryAdd for the procedural interface

 itk::TernaryAddImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkTernaryAddImageFilter.h
";

%feature("docstring")  itk::simple::TernaryAddImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::TernaryAddImageFilter::Execute "
";

%feature("docstring")  itk::simple::TernaryAddImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TernaryAddImageFilter::TernaryAddImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::TernaryAddImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::TernaryAddImageFilter::~TernaryAddImageFilter "

Destructor

";


%feature("docstring") itk::simple::TernaryMagnitudeImageFilter "

Compute the pixel-wise magnitude of three images.


This class is templated over the types of the three input images and
the type of the output image. Numeric conversions (castings) are done
by the C++ defaults.
See:
 itk::simple::TernaryMagnitude for the procedural interface

 itk::TernaryMagnitudeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkTernaryMagnitudeImageFilter.h
";

%feature("docstring")  itk::simple::TernaryMagnitudeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::TernaryMagnitudeImageFilter::Execute "
";

%feature("docstring")  itk::simple::TernaryMagnitudeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TernaryMagnitudeImageFilter::TernaryMagnitudeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::TernaryMagnitudeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::TernaryMagnitudeImageFilter::~TernaryMagnitudeImageFilter "

Destructor

";


%feature("docstring") itk::simple::TernaryMagnitudeSquaredImageFilter "

Compute the pixel-wise squared magnitude of three images.


This class is templated over the types of the three input images and
the type of the output image. Numeric conversions (castings) are done
by the C++ defaults.
See:
 itk::simple::TernaryMagnitudeSquared for the procedural interface

 itk::TernaryMagnitudeSquaredImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkTernaryMagnitudeSquaredImageFilter.h
";

%feature("docstring")  itk::simple::TernaryMagnitudeSquaredImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::TernaryMagnitudeSquaredImageFilter::Execute "
";

%feature("docstring")  itk::simple::TernaryMagnitudeSquaredImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TernaryMagnitudeSquaredImageFilter::TernaryMagnitudeSquaredImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::TernaryMagnitudeSquaredImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::TernaryMagnitudeSquaredImageFilter::~TernaryMagnitudeSquaredImageFilter "

Destructor

";


%feature("docstring") itk::simple::ThresholdImageFilter "

Set image values to a user-specified value if they are below, above,
or between simple threshold values.


ThresholdImageFilter sets image values to a user-specified \"outside\" value (by default,
\"black\") if the image values are below, above, or between simple
threshold values.

The available methods are:

ThresholdAbove() : The values greater than the threshold value are set
to OutsideValue

ThresholdBelow() : The values less than the threshold value are set to
OutsideValue

ThresholdOutside() : The values outside the threshold range (less than
lower or greater than upper) are set to OutsideValue

Note that these definitions indicate that pixels equal to the
threshold value are not set to OutsideValue in any of these methods

The pixels must support the operators >= and <=.
See:
 itk::simple::Threshold for the procedural interface

 itk::ThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkThresholdImageFilter.h
";

%feature("docstring")  itk::simple::ThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::ThresholdImageFilter::GetLower "

Set/Get methods to set the lower threshold.

";

%feature("docstring")  itk::simple::ThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::ThresholdImageFilter::GetUpper "

Set/Get methods to set the upper threshold.

";

%feature("docstring")  itk::simple::ThresholdImageFilter::SetLower "

Set/Get methods to set the lower threshold.

";

%feature("docstring")  itk::simple::ThresholdImageFilter::SetOutsideValue "

The pixel type must support comparison operators. Set the \"outside\"
pixel value. The default value NumericTraits<PixelType>::ZeroValue() .

";

%feature("docstring")  itk::simple::ThresholdImageFilter::SetUpper "

Set/Get methods to set the upper threshold.

";

%feature("docstring")  itk::simple::ThresholdImageFilter::ThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ThresholdImageFilter::~ThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::ThresholdMaximumConnectedComponentsImageFilter "

Finds the threshold value of an image based on maximizing the number
of objects in the image that are larger than a given minimal size.



This method is based on Topological Stable State Thresholding to
calculate the threshold set point. This method is particularly
effective when there are a large number of objects in a microscopy
image. Compiling in Debug mode and enable the debug flag for this
filter to print debug information to see how the filter focuses in on
a threshold value. Please see the Insight Journal's MICCAI 2005
workshop for a complete description. References are below.
Parameters
The MinimumObjectSizeInPixels parameter is controlled through the
class Get/SetMinimumObjectSizeInPixels() method. Similar to the
standard itk::BinaryThresholdImageFilter the Get/SetInside and Get/SetOutside values of the threshold can be
set. The GetNumberOfObjects() and GetThresholdValue() methods return
the number of objects above the minimum pixel size and the calculated
threshold value.
Automatic Thresholding in ITK
There are multiple methods to automatically calculate the threshold
intensity value of an image. As of version 4.0, ITK has a Thresholding
( ITKThresholding ) module which contains numerous automatic
thresholding methods.implements two of these. Topological Stable State
Thresholding works well on images with a large number of objects to be
counted.
References:
1) Urish KL, August J, Huard J. \"Unsupervised segmentation for
myofiber counting in immunoflourescent images\". Insight Journal. ISC
/NA-MIC/MICCAI Workshop on Open-Source Software (2005) Dspace handle: https://hdl.handle.net/1926/48 2) Pikaz A, Averbuch, A. \"Digital image thresholding based on
topological stable-state\". Pattern Recognition, 29(5): 829-843, 1996.

Questions: email Ken Urish at ken.urish(at)gmail.com Please cc the itk
list serve for archival purposes.

See:
 itk::simple::ThresholdMaximumConnectedComponents for the procedural interface

 itk::ThresholdMaximumConnectedComponentsImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkThresholdMaximumConnectedComponentsImageFilter.h
";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::GetInsideValue "

The following Set/Get methods are for the binary threshold function.
This class automatically calculates the lower threshold boundary. The
upper threshold boundary, inside value, and outside value can be
defined by the user, however the standard values are used as default
if not set by the user. The default value of the: Inside value is the
maximum pixel type intensity. Outside value is the minimum pixel type
intensity. Upper threshold boundary is the maximum pixel type
intensity.

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::GetMinimumObjectSizeInPixels "

The pixel type must support comparison operators. Set the minimum
pixel area used to count objects on the image. Thus, only objects that
have a pixel area greater than the minimum pixel area will be counted
as an object in the optimization portion of this filter. Essentially,
it eliminates noise from being counted as an object. The default value
is zero.

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::GetOutsideValue "

The following Set/Get methods are for the binary threshold function.
This class automatically calculates the lower threshold boundary. The
upper threshold boundary, inside value, and outside value can be
defined by the user, however the standard values are used as default
if not set by the user. The default value of the: Inside value is the
maximum pixel type intensity. Outside value is the minimum pixel type
intensity. Upper threshold boundary is the maximum pixel type
intensity.

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::GetUpperBoundary "

The following Set/Get methods are for the binary threshold function.
This class automatically calculates the lower threshold boundary. The
upper threshold boundary, inside value, and outside value can be
defined by the user, however the standard values are used as default
if not set by the user. The default value of the: Inside value is the
maximum pixel type intensity. Outside value is the minimum pixel type
intensity. Upper threshold boundary is the maximum pixel type
intensity.

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::SetInsideValue "

The following Set/Get methods are for the binary threshold function.
This class automatically calculates the lower threshold boundary. The
upper threshold boundary, inside value, and outside value can be
defined by the user, however the standard values are used as default
if not set by the user. The default value of the: Inside value is the
maximum pixel type intensity. Outside value is the minimum pixel type
intensity. Upper threshold boundary is the maximum pixel type
intensity.

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::SetMinimumObjectSizeInPixels "

The pixel type must support comparison operators. Set the minimum
pixel area used to count objects on the image. Thus, only objects that
have a pixel area greater than the minimum pixel area will be counted
as an object in the optimization portion of this filter. Essentially,
it eliminates noise from being counted as an object. The default value
is zero.

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::SetOutsideValue "

The following Set/Get methods are for the binary threshold function.
This class automatically calculates the lower threshold boundary. The
upper threshold boundary, inside value, and outside value can be
defined by the user, however the standard values are used as default
if not set by the user. The default value of the: Inside value is the
maximum pixel type intensity. Outside value is the minimum pixel type
intensity. Upper threshold boundary is the maximum pixel type
intensity.

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::SetUpperBoundary "

The following Set/Get methods are for the binary threshold function.
This class automatically calculates the lower threshold boundary. The
upper threshold boundary, inside value, and outside value can be
defined by the user, however the standard values are used as default
if not set by the user. The default value of the: Inside value is the
maximum pixel type intensity. Outside value is the minimum pixel type
intensity. Upper threshold boundary is the maximum pixel type
intensity.

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::ThresholdMaximumConnectedComponentsImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ThresholdMaximumConnectedComponentsImageFilter::~ThresholdMaximumConnectedComponentsImageFilter "

Destructor

";


%feature("docstring") itk::simple::ThresholdSegmentationLevelSetImageFilter "

Segments structures in images based on intensity values.


IMPORTANT
The SegmentationLevelSetImageFilter class and the ThresholdSegmentationLevelSetFunction class contain additional information necessary to the full
understanding of how to use this filter.
OVERVIEW
This class is a level set method segmentation filter. It constructs a
speed function which is close to zero at the upper and lower bounds of
an intensity window, effectively locking the propagating front onto
those edges. Elsewhere, the front will propagate quickly.
INPUTS
This filter requires two inputs. The first input is a seed image. This
seed image must contain an isosurface that you want to use as the seed
for your segmentation. It can be a binary, graylevel, or floating
point image. The only requirement is that it contain a closed
isosurface that you will identify as the seed by setting the
IsosurfaceValue parameter of the filter. For a binary image you will
want to set your isosurface value halfway between your on and off
values (i.e. for 0's and 1's, use an isosurface value of 0.5).

The second input is the feature image. This is the image from which
the speed function will be calculated. For most applications, this is
the image that you want to segment. The desired isosurface in your
seed image should lie within the region of your feature image that you
are trying to segment. Note that this filter does no preprocessing of
the feature image before thresholding.

See SegmentationLevelSetImageFilter for more information on Inputs.
OUTPUTS
The filter outputs a single, scalar, real-valued image. Positive
values in the output image are inside the segmented region and
negative values in the image are outside of the inside region. The
zero crossings of the image correspond to the position of the level
set front.

See SparseFieldLevelSetImageFilter and SegmentationLevelSetImageFilter for more information.
PARAMETERS
In addition to parameters described in SegmentationLevelSetImageFilter , this filter adds the UpperThreshold and LowerThreshold. See ThresholdSegmentationLevelSetFunction for a description of how these values affect the segmentation.

See:
 SegmentationLevelSetImageFilter

 ThresholdSegmentationLevelSetFunction ,

 SparseFieldLevelSetImageFilter

 itk::simple::ThresholdSegmentationLevelSet for the procedural interface

 itk::ThresholdSegmentationLevelSetImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkThresholdSegmentationLevelSetImageFilter.h
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::Execute "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetCurvatureScaling "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetElapsedIterations "

Number of iterations run.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetLowerThreshold "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetMaximumRMSError "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetNumberOfIterations "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetPropagationScaling "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetRMSChange "

The Root Mean Square of the levelset upon termination.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::GetUpperThreshold "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::ReverseExpansionDirectionOff "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::ReverseExpansionDirectionOn "

Set the value of ReverseExpansionDirection to true or false
respectfully.

";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::SetCurvatureScaling "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::SetLowerThreshold "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::SetMaximumRMSError "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::SetNumberOfIterations "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::SetPropagationScaling "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::SetReverseExpansionDirection "
";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::SetUpperThreshold "

Get/Set the threshold values that will be used to calculate the speed
function.

";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::ThresholdSegmentationLevelSetImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ThresholdSegmentationLevelSetImageFilter::~ThresholdSegmentationLevelSetImageFilter "

Destructor

";


%feature("docstring") itk::simple::TikhonovDeconvolutionImageFilter "

An inverse deconvolution filter regularized in the Tikhonov sense.


The Tikhonov deconvolution filter is the inverse deconvolution filter
with a regularization term added to the denominator. The filter
minimizes the equation \\\\[ ||\\\\hat{f} \\\\otimes h - g||_{L_2}^2 + \\\\mu||\\\\hat{f}||^2
\\\\] where $\\\\hat{f}$ is the estimate of the unblurred image, $h$ is the blurring kernel, $g$ is the blurred image, and $\\\\mu$ is a non-negative real regularization function.

The filter applies a kernel described in the Fourier domain as $H^*(\\\\omega) / (|H(\\\\omega)|^2 + \\\\mu)$ where $H(\\\\omega)$ is the Fourier transform of $h$ . The term $\\\\mu$ is called RegularizationConstant in this filter. If $\\\\mu$ is set to zero, this filter is equivalent to the InverseDeconvolutionImageFilter .


Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France

Cory Quammen, The University of North Carolina at Chapel Hill

See:
 itk::simple::TikhonovDeconvolution for the procedural interface

 itk::TikhonovDeconvolutionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkTikhonovDeconvolutionImageFilter.h
";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::GetBoundaryCondition "
";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::GetNormalize "
";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::GetOutputRegionMode "
";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::GetRegularizationConstant "

The regularization factor. Larger values reduce the dominance of noise
in the solution, but results in higher approximation error in the
deblurred image. Default value is 0.0, yielding the same results as
the InverseDeconvolutionImageFilter .

";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::NormalizeOff "
";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::NormalizeOn "

Set the value of Normalize to true or false respectfully.

";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::SetBoundaryCondition "
";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::SetNormalize "

Normalize the output image by the sum of the kernel components

";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::SetOutputRegionMode "
";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::SetRegularizationConstant "

The regularization factor. Larger values reduce the dominance of noise
in the solution, but results in higher approximation error in the
deblurred image. Default value is 0.0, yielding the same results as
the InverseDeconvolutionImageFilter .

";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::TikhonovDeconvolutionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::TikhonovDeconvolutionImageFilter::~TikhonovDeconvolutionImageFilter "

Destructor

";


%feature("docstring") itk::simple::TileImageFilter "

Tile multiple input images into a single output image.


This filter will tile multiple images using a user-specified layout.
The tile sizes will be large enough to accommodate the largest image
for each tile. The layout is specified with the SetLayout method. The
layout has the same dimension as the output image. If all entries of
the layout are positive, the tiled output will contain the exact
number of tiles. If the layout contains a 0 in the last dimension, the
filter will compute a size that will accommodate all of the images.
Empty tiles are filled with the value specified with the SetDefault
value method. The input images must have a dimension less than or
equal to the output image. The output image have a larger dimension
than the input images. This filter can be used to create a volume from
a series of inputs by specifying a layout of 1,1,0.


See:
 itk::simple::Tile for the procedural interface


C++ includes: sitkTileImageFilter.h
";

%feature("docstring")  itk::simple::TileImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::TileImageFilter::Execute "
";

%feature("docstring")  itk::simple::TileImageFilter::Execute "
";

%feature("docstring")  itk::simple::TileImageFilter::Execute "
";

%feature("docstring")  itk::simple::TileImageFilter::Execute "
";

%feature("docstring")  itk::simple::TileImageFilter::Execute "
";

%feature("docstring")  itk::simple::TileImageFilter::GetDefaultPixelValue "

Get the pixel value for locations that are not covered by an input
image.

";

%feature("docstring")  itk::simple::TileImageFilter::GetLayout "

Set/Get the layout of the tiles. If the last Layout value is 0, the
filter will compute a value that will accommodate all of the images.

";

%feature("docstring")  itk::simple::TileImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TileImageFilter::SetDefaultPixelValue "

Set the pixel value for locations that are not covered by an input
image. The default default pixel value is Zero.

";

%feature("docstring")  itk::simple::TileImageFilter::SetLayout "

Set/Get the layout of the tiles. If the last Layout value is 0, the
filter will compute a value that will accommodate all of the images.

";

%feature("docstring")  itk::simple::TileImageFilter::TileImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::TileImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::TileImageFilter::~TileImageFilter "

Destructor

";


%feature("docstring") itk::simple::TobogganImageFilter "

toboggan image segmentation The Toboggan segmentation takes a gradient
magnitude image as input and produces an (over-)segmentation of the
image based on connecting each pixel to a local minimum of gradient.
It is roughly equivalent to a watershed segmentation of the lowest
level.


The output is a 4 connected labeled map of the image.
See:
 itk::simple::Toboggan for the procedural interface

 itk::TobogganImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkTobogganImageFilter.h
";

%feature("docstring")  itk::simple::TobogganImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::TobogganImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TobogganImageFilter::TobogganImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::TobogganImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::TobogganImageFilter::~TobogganImageFilter "

Destructor

";


%feature("docstring") itk::simple::Transform "

A simplified wrapper around a variety of ITK transforms.


The interface to ITK transform objects to be used with the ImageRegistrationMethod, ResampleImageFilter and other SimpleITK process objects. The transforms are designed to
have a serialized array of parameters to facilitate optimization for
registration.

Provides a base class interface to any type of ITK transform. Objects
of this type may have their interface converted to a derived interface
while keeping the same reference to the ITK object.

Additionally, this class provides a basic interface to a composite
transforms.


See:
 itk::CompositeTransform


C++ includes: sitkTransform.h
";

%feature("docstring")  itk::simple::Transform::GetDimension "

Return the dimension of the Transform ( 2D or 3D )

";

%feature("docstring")  itk::simple::Transform::GetInverse "

Return a new inverse transform of the same type as this.


Creates a new transform object and tries to set the value to the
inverse. As not all transform types have inverse and some transforms
are not invertible, an exception will be throw is there is no inverse.

";

%feature("docstring")  itk::simple::Transform::GetName "

return user readable name for the SimpleITK transform

";

%feature("docstring")  itk::simple::Transform::GetNumberOfFixedParameters "

Get the number of fixed parameters

";

%feature("docstring")  itk::simple::Transform::GetNumberOfParameters "

Return the number of optimizable parameters

";

%feature("docstring")  itk::simple::Transform::GetTransformEnum "

Get the TransformEnum of the underlying Transform.


A SimpleITK Transform object can internally hold any ITK transform. This method returns the
TransformEnum representing the internal ITK transform. This value may
be used to identify which SimpleITK class the transform can be
converted to.

";

%feature("docstring")  itk::simple::Transform::IsLinear "
";

%feature("docstring")  itk::simple::Transform::MakeUnique "

Performs actually coping if needed to make object unique.


The Transform class by default performs lazy coping and assignment. This method
make sure that coping actually happens to the itk::Transform pointed to is only pointed to by this object.

";

%feature("docstring")  itk::simple::Transform::SetIdentity "
";

%feature("docstring")  itk::simple::Transform::SetInverse "

Try to change the current transform to it's inverse.


If the transform has an inverse, i.e. non-singular linear transforms,
then a new ITK transform is created of the same type and this object
is set to it.

However not all transform have a direct inverse, if the inverse does
not exist or fails false will be returned and this transform will not
be modified.

";

%feature("docstring")  itk::simple::Transform::ToString "
";

%feature("docstring")  itk::simple::Transform::Transform "

By default a 3-d identity transform is constructed.

";

%feature("docstring")  itk::simple::Transform::Transform "

Construct a SimpleITK Transform from a pointer to an ITK composite transform.

";

%feature("docstring")  itk::simple::Transform::Transform "
";

%feature("docstring")  itk::simple::Transform::Transform "

Construct a specific transformation.


Deprecated
This constructor will be removed in future releases.


";

%feature("docstring")  itk::simple::Transform::Transform "

Use an image to construct a transform.


The input displacement image is transferred to the constructed
transform object. The input image is modified to be a default
constructed Image object.

Only the sitkDisplacementField transformation type can currently be
constructed this way. Image must be of sitkVectorFloat64 pixel type with the number of components
equal to the image dimension.

Deprecated
This constructor will be removed in future releases.


";

%feature("docstring")  itk::simple::Transform::TransformPoint "

Apply transform to a point.

The dimension of the point must match the transform.

";

%feature("docstring")  itk::simple::Transform::TransformVector "

Apply transform to a vector at a point.

The ITK concept of a vector is a direction at a specific point, for
example the difference between two points is a vector.

For linear transforms the point does not matter, in general the vector
is transformed by the Jacobian with respect to point position.

The dimension of the vector and point must match the transform.

";

%feature("docstring")  itk::simple::Transform::WriteTransform "
";

%feature("docstring")  itk::simple::Transform::~Transform "
";


%feature("docstring") itk::simple::TransformToDisplacementFieldFilter "

Generate a displacement field from a coordinate transform.


Output information (spacing, size and direction) for the output image
should be set. This information has the normal defaults of unit
spacing, zero origin and identity direction. Optionally, the output
information can be obtained from a reference image. If the reference
image is provided and UseReferenceImage is On, then the spacing,
origin and direction of the reference image will be used.

Since this filter produces an image which is a different size than its
input, it needs to override several of the methods defined in ProcessObject in order to properly manage the pipeline execution model. In
particular, this filter overrides ProcessObject::GenerateOutputInformation() .

This filter is implemented as a multithreaded filter. It provides a
ThreadedGenerateData() method for its implementation.


Marius Staring, Leiden University Medical Center, The Netherlands.
 This class was taken from the Insight Journal paper: https://hdl.handle.net/1926/1387
See:
 itk::simple::TransformToDisplacementFieldFilter for the procedural interface

 itk::TransformToDisplacementFieldFilter for the Doxygen on the original ITK class.


C++ includes: sitkTransformToDisplacementFieldFilter.h
";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::GetOutputDirection "

Set the output direction cosine matrix.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::GetOutputOrigin "

Get the output image origin.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::GetOutputPixelType "

Get the ouput pixel type.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::GetOutputSpacing "

Get the output image spacing.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::GetSize "

Set/Get the size of the output image.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::SetOutputDirection "

Set the output direction cosine matrix.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::SetOutputOrigin "

Set the output image origin.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::SetOutputPixelType "

Set the output pixel type, only sitkVectorFloat32 and
sitkVectorFloat64 are supported.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::SetOutputSpacing "

Set the output image spacing.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::SetReferenceImage "

This methods sets the size, origin, spacing and direction to that of
the provided image

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::SetSize "

Set/Get the size of the output image.

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::TransformToDisplacementFieldFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::TransformToDisplacementFieldFilter::~TransformToDisplacementFieldFilter "

Destructor

";


%feature("docstring") itk::simple::TranslationTransform "

Translation of a 2D or 3D coordinate space.



See:
 itk::TranslationTransform


C++ includes: sitkTranslationTransform.h
";

%feature("docstring")  itk::simple::TranslationTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TranslationTransform::GetOffset "
";

%feature("docstring")  itk::simple::TranslationTransform::SetOffset "
";

%feature("docstring")  itk::simple::TranslationTransform::TranslationTransform "
";

%feature("docstring")  itk::simple::TranslationTransform::TranslationTransform "
";

%feature("docstring")  itk::simple::TranslationTransform::TranslationTransform "
";

%feature("docstring")  itk::simple::TranslationTransform::~TranslationTransform "
";


%feature("docstring") itk::simple::TriangleThresholdImageFilter "

Threshold an image using the Triangle Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the TriangleThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::TriangleThreshold for the procedural interface

 itk::TriangleThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkTriangleThresholdImageFilter.h
";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::TriangleThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::TriangleThresholdImageFilter::~TriangleThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::UnaryMinusImageFilter "

Implements pixel-wise generic operation on one image.


This class is parameterized over the type of the input image and the
type of the output image. It is also parameterized by the operation to
be applied, using a Functor style.

UnaryFunctorImageFilter allows the output dimension of the filter to be larger than the input
dimension. Thus subclasses of the UnaryFunctorImageFilter (like the CastImageFilter ) can be used to promote a 2D image to a 3D image, etc.


See:
 UnaryGeneratorImageFilter

 BinaryFunctorImageFilter TernaryFunctorImageFilter

 itk::simple::UnaryMinus for the procedural interface

 itk::UnaryFunctorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkUnaryMinusImageFilter.h
";

%feature("docstring")  itk::simple::UnaryMinusImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::UnaryMinusImageFilter::Execute "
";

%feature("docstring")  itk::simple::UnaryMinusImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::UnaryMinusImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::UnaryMinusImageFilter::UnaryMinusImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::UnaryMinusImageFilter::~UnaryMinusImageFilter "

Destructor

";


%feature("docstring") itk::simple::UnsharpMaskImageFilter "

Edge enhancement filter.


This filter subtracts a smoothed version of the image from the image
to achieve the edge enhancing effect. https://en.wikipedia.org/w/index.php?title=Unsharp_masking&oldid=75048
6803#Photographic_unsharp_masking

It has configurable amount, radius (sigma) and threshold, and whether
to clamp the resulting values to the range of output type.

Formula: sharpened=original+[abs(original-blurred)-threshold]*amount

If clamping is turned off (it is on by default), casting to output
pixel format is done using C++ defaults, meaning that values are not
clamped but rather wrap around e.g. 260 -> 4 (unsigned char).


See:
 ImageToImageFilter

 SmoothingRecursiveGaussianImageFilter

 RescaleIntensityImageFilter

 itk::simple::UnsharpMask for the procedural interface

 itk::UnsharpMaskImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkUnsharpMaskImageFilter.h
";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::GetAmount "

Set/Get amount of enhancement. Usual range: 0.1 to 2.0. Default: 0.5.

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::GetSigmas "

Set/Get Sigma values measured in the units of image spacing. Default:
1.0.

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::GetThreshold "

Set/Get threshold for enhancement. Default: 0.

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::SetAmount "

Set/Get amount of enhancement. Usual range: 0.1 to 2.0. Default: 0.5.

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::SetSigmas "

Custom public declarations

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::SetSigmas "

Set/Get Sigma values measured in the units of image spacing. Default:
1.0.

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::SetThreshold "

Set/Get threshold for enhancement. Default: 0.

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::UnsharpMaskImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::UnsharpMaskImageFilter::~UnsharpMaskImageFilter "

Destructor

";


%feature("docstring") itk::simple::ValuedRegionalMaximaImageFilter "

Transforms the image so that any pixel that is not a regional maxima
is set to the minimum value for the pixel type. Pixels that are
regional maxima retain their value.


Regional maxima are flat zones surrounded by pixels of lower value. A
completely flat image will be marked as a regional maxima by this
filter.

This code was contributed in the Insight Journal paper: \"Finding
regional extrema - methods and performance\" by Beare R., Lehmann G. https://hdl.handle.net/1926/153 http://www.insight-journal.org/browse/publication/65


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

See:
 ValuedRegionalMinimaImageFilter

 ValuedRegionalExtremaImageFilter

 HMinimaImageFilter

 itk::simple::ValuedRegionalMaxima for the procedural interface

 itk::ValuedRegionalMaximaImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkValuedRegionalMaximaImageFilter.h
";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::GetFlat "

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::GetFullyConnected "
";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::SetFullyConnected "
";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::ValuedRegionalMaximaImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ValuedRegionalMaximaImageFilter::~ValuedRegionalMaximaImageFilter "

Destructor

";


%feature("docstring") itk::simple::ValuedRegionalMinimaImageFilter "

Transforms the image so that any pixel that is not a regional minima
is set to the maximum value for the pixel type. Pixels that are
regional minima retain their value.


Regional minima are flat zones surrounded by pixels of higher value. A
completely flat image will be marked as a regional minima by this
filter.

This code was contributed in the Insight Journal paper: \"Finding
regional extrema - methods and performance\" by Beare R., Lehmann G. https://hdl.handle.net/1926/153 http://www.insight-journal.org/browse/publication/65


Richard Beare. Department of Medicine, Monash University, Melbourne,
Australia.

See:
 ValuedRegionalMaximaImageFilter , ValuedRegionalExtremaImageFilter ,

 HMinimaImageFilter

 itk::simple::ValuedRegionalMinima for the procedural interface

 itk::ValuedRegionalMinimaImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkValuedRegionalMinimaImageFilter.h
";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::GetFlat "

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::GetFullyConnected "
";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::SetFullyConnected "
";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::ValuedRegionalMinimaImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ValuedRegionalMinimaImageFilter::~ValuedRegionalMinimaImageFilter "

Destructor

";


%feature("docstring") itk::simple::VectorConfidenceConnectedImageFilter "

Segment pixels with similar statistics using connectivity.


This filter extracts a connected set of pixels whose pixel intensities
are consistent with the pixel statistics of a seed point. The mean and
variance across a neighborhood (8-connected, 26-connected, etc.) are
calculated for a seed point. Then pixels connected to this seed point
whose values are within the confidence interval for the seed point are
grouped. The width of the confidence interval is controlled by the
\"Multiplier\" variable (the confidence interval is the mean plus or
minus the \"Multiplier\" times the standard deviation). If the
intensity variations across a segment were gaussian, a \"Multiplier\"
setting of 2.5 would define a confidence interval wide enough to
capture 99% of samples in the segment.

After this initial segmentation is calculated, the mean and variance
are re-calculated. All the pixels in the previous segmentation are
used to calculate the mean the standard deviation (as opposed to using
the pixels in the neighborhood of the seed point). The segmentation is
then recalculted using these refined estimates for the mean and
variance of the pixel values. This process is repeated for the
specified number of iterations. Setting the \"NumberOfIterations\" to
zero stops the algorithm after the initial segmentation from the seed
point.

NOTE: the lower and upper threshold are restricted to lie within the
valid numeric limits of the input data pixel type. Also, the limits
may be adjusted to contain the seed point's intensity.
See:
 itk::simple::VectorConfidenceConnected for the procedural interface

 itk::VectorConfidenceConnectedImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkVectorConfidenceConnectedImageFilter.h
";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::AddSeed "

Add SeedList point.

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::ClearSeeds "

Remove all SeedList points.

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::GetCovariance "

Get the Covariance matrix computed during the segmentation

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::GetInitialNeighborhoodRadius "

Get/Set the radius of the neighborhood over which the statistics are
evaluated

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::GetMean "

Get the Mean Vector computed during the segmentation

This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::GetMultiplier "

Set/Get the multiplier to define the confidence interval. Multiplier
can be anything greater than zero. A typical value is 2.5

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::GetNumberOfIterations "

Set/Get the number of iterations

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::GetReplaceValue "

Set/Get value to replace thresholded pixels

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::GetSeedList "

Get list of seeds.

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::SetInitialNeighborhoodRadius "

Get/Set the radius of the neighborhood over which the statistics are
evaluated

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::SetMultiplier "

Set/Get the multiplier to define the confidence interval. Multiplier
can be anything greater than zero. A typical value is 2.5

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::SetNumberOfIterations "

Set/Get the number of iterations

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::SetReplaceValue "

Set/Get value to replace thresholded pixels

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::SetSeedList "

Set list of image indexes for seeds.

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::VectorConfidenceConnectedImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::VectorConfidenceConnectedImageFilter::~VectorConfidenceConnectedImageFilter "

Destructor

";


%feature("docstring") itk::simple::VectorConnectedComponentImageFilter "

A connected components filter that labels the objects in a vector
image. Two vectors are pointing similar directions if one minus their
dot product is less than a threshold. Vectors that are 180 degrees out
of phase are similar. Assumes that vectors are normalized.



See:
 itk::simple::VectorConnectedComponent for the procedural interface

 itk::VectorConnectedComponentImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkVectorConnectedComponentImageFilter.h
";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::FullyConnectedOff "
";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::FullyConnectedOn "

Set the value of FullyConnected to true or false respectfully.

";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::GetDistanceThreshold "
";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::GetFullyConnected "
";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::SetDistanceThreshold "
";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::SetFullyConnected "
";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::VectorConnectedComponentImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::VectorConnectedComponentImageFilter::~VectorConnectedComponentImageFilter "

Destructor

";


%feature("docstring") itk::simple::VectorIndexSelectionCastImageFilter "

Extracts the selected index of the vector that is the input pixel
type.


This filter is templated over the input image type and output image
type.

The filter expect the input image pixel type to be a vector and the
output image pixel type to be a scalar. The only requirement on the
type used for representing the vector is that it must provide an
operator[].


See:
 ComposeImageFilter

 itk::simple::VectorIndexSelectionCast for the procedural interface

 itk::VectorIndexSelectionCastImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkVectorIndexSelectionCastImageFilter.h
";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::Execute "
";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::GetIndex "

Get/Set methods for the index

";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::GetOutputPixelType "

Get the ouput pixel type.

";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::SetIndex "

Get/Set methods for the index

";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::SetOutputPixelType "

Set the output pixel type of the scalar component to extract.

";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::VectorIndexSelectionCastImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::VectorIndexSelectionCastImageFilter::~VectorIndexSelectionCastImageFilter "

Destructor

";


%feature("docstring") itk::simple::VectorMagnitudeImageFilter "

Take an image of vectors as input and produce an image with the
magnitude of those vectors.


The filter expects the input image pixel type to be a vector and the
output image pixel type to be a scalar.

This filter assumes that the PixelType of the input image is a
VectorType that provides a GetNorm() method.
See:
 itk::simple::VectorMagnitude for the procedural interface

 itk::VectorMagnitudeImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkVectorMagnitudeImageFilter.h
";

%feature("docstring")  itk::simple::VectorMagnitudeImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::VectorMagnitudeImageFilter::Execute "
";

%feature("docstring")  itk::simple::VectorMagnitudeImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::VectorMagnitudeImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::VectorMagnitudeImageFilter::VectorMagnitudeImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::VectorMagnitudeImageFilter::~VectorMagnitudeImageFilter "

Destructor

";


%feature("docstring") itk::simple::Version "

Version info for SimpleITK.

C++ includes: sitkVersion.h
";

%feature("docstring")  itk::simple::Version::ToString "
";


%feature("docstring") itk::simple::VersorRigid3DTransform "

A rotation as a versor around a fixed center with translation of a 3D
coordinate space.



See:
 itk::VersorRigid3DTransform


C++ includes: sitkVersorRigid3DTransform.h
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::GetCenter "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::GetMatrix "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::VersorRigid3DTransform::GetTranslation "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::GetVersor "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::VersorRigid3DTransform::SetMatrix "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::SetRotation "

parameter

";

%feature("docstring")  itk::simple::VersorRigid3DTransform::SetRotation "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::SetTranslation "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::Translate "

additional methods

";

%feature("docstring")  itk::simple::VersorRigid3DTransform::VersorRigid3DTransform "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::VersorRigid3DTransform "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::VersorRigid3DTransform "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::VersorRigid3DTransform "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::VersorRigid3DTransform "
";

%feature("docstring")  itk::simple::VersorRigid3DTransform::~VersorRigid3DTransform "
";


%feature("docstring") itk::simple::VersorTransform "

A 3D rotation transform with rotation as a versor around a fixed
center.



See:
 itk::VersorTransform


C++ includes: sitkVersorTransform.h
";

%feature("docstring")  itk::simple::VersorTransform::GetCenter "
";

%feature("docstring")  itk::simple::VersorTransform::GetMatrix "

additional methods

";

%feature("docstring")  itk::simple::VersorTransform::GetName "

Name of this class

";

%feature("docstring")  itk::simple::VersorTransform::GetVersor "
";

%feature("docstring")  itk::simple::VersorTransform::SetCenter "

fixed parameter

";

%feature("docstring")  itk::simple::VersorTransform::SetMatrix "
";

%feature("docstring")  itk::simple::VersorTransform::SetRotation "

parameter

";

%feature("docstring")  itk::simple::VersorTransform::SetRotation "
";

%feature("docstring")  itk::simple::VersorTransform::VersorTransform "
";

%feature("docstring")  itk::simple::VersorTransform::VersorTransform "
";

%feature("docstring")  itk::simple::VersorTransform::VersorTransform "
";

%feature("docstring")  itk::simple::VersorTransform::VersorTransform "
";

%feature("docstring")  itk::simple::VersorTransform::VersorTransform "
";

%feature("docstring")  itk::simple::VersorTransform::~VersorTransform "
";


%feature("docstring") itk::simple::VotingBinaryHoleFillingImageFilter "

Fills in holes and cavities by applying a voting operation on each
pixel.



See:
 Image

 VotingBinaryImageFilter

 VotingBinaryIterativeHoleFillingImageFilter

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::VotingBinaryHoleFilling for the procedural interface

 itk::VotingBinaryHoleFillingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkVotingBinaryHoleFillingImageFilter.h
";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::GetBackgroundValue "
";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::GetForegroundValue "
";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::GetMajorityThreshold "

Majority threshold. It is the number of pixels over 50% that will
decide whether an OFF pixel will become ON or not. For example, if the
neighborhood of a pixel has 124 pixels (excluding itself), the 50%
will be 62, and if you set upd a Majority threshold of 5, that means
that the filter will require 67 or more neighbor pixels to be ON in
order to switch the current OFF pixel to ON. The default value is 1.

";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::GetRadius "
";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::SetBackgroundValue "
";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::SetForegroundValue "
";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::SetMajorityThreshold "

Majority threshold. It is the number of pixels over 50% that will
decide whether an OFF pixel will become ON or not. For example, if the
neighborhood of a pixel has 124 pixels (excluding itself), the 50%
will be 62, and if you set upd a Majority threshold of 5, that means
that the filter will require 67 or more neighbor pixels to be ON in
order to switch the current OFF pixel to ON. The default value is 1.

";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::SetRadius "
";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::VotingBinaryHoleFillingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::VotingBinaryHoleFillingImageFilter::~VotingBinaryHoleFillingImageFilter "

Destructor

";


%feature("docstring") itk::simple::VotingBinaryImageFilter "

Applies a voting operation in a neighborhood of each pixel.



Pixels which are not Foreground or Background will remain unchanged.

See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::VotingBinary for the procedural interface

 itk::VotingBinaryImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkVotingBinaryImageFilter.h
";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::GetBackgroundValue "

Get the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::GetBirthThreshold "

Birth threshold. Pixels that are OFF will turn ON when the number of
neighbors ON is larger than the value defined in this threshold.

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::GetForegroundValue "

Get the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::GetRadius "

Get the radius of the neighborhood used to compute the median

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::GetSurvivalThreshold "

Survival threshold. Pixels that are ON will turn OFF when the number
of neighbors ON is smaller than the value defined in this survival
threshold.

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::SetBackgroundValue "

Set the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::SetBirthThreshold "

Birth threshold. Pixels that are OFF will turn ON when the number of
neighbors ON is larger than the value defined in this threshold.

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::SetForegroundValue "

Set the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::SetRadius "

Set the radius of the neighborhood used to compute the median.

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::SetSurvivalThreshold "

Survival threshold. Pixels that are ON will turn OFF when the number
of neighbors ON is smaller than the value defined in this survival
threshold.

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::VotingBinaryImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::VotingBinaryImageFilter::~VotingBinaryImageFilter "

Destructor

";


%feature("docstring") itk::simple::VotingBinaryIterativeHoleFillingImageFilter "

Fills in holes and cavities by iteratively applying a voting
operation.


This filter uses internally the VotingBinaryHoleFillingImageFilter , and runs it iteratively until no pixels are being changed or until
it reaches the maximum number of iterations. The purpose of the filter
is to fill in holes of medium size (tens of pixels in radius). In
principle the number of iterations is related to the size of the holes
to be filled in. The larger the holes, the more iteration must be run
with this filter in order to fill in the full hole. The size of the
neighborhood is also related to the curvature of the hole borders and
therefore the hole size. Note that as a collateral effect this filter
may also fill in cavities in the external side of structures.

This filter is templated over a single image type because the output
image type must be the same as the input image type. This is required
in order to make the iterations possible, since the output image of
one iteration is taken as the input image for the next iteration.


See:
 Image

 VotingBinaryImageFilter

 VotingBinaryHoleFillingImageFilter

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::VotingBinaryIterativeHoleFilling for the procedural interface

 itk::VotingBinaryIterativeHoleFillingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkVotingBinaryIterativeHoleFillingImageFilter.h
";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::GetBackgroundValue "

Get the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::GetForegroundValue "

Get the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::GetMajorityThreshold "

Majority threshold. It is the number of pixels over 50% that will
decide whether an OFF pixel will become ON or not. For example, if the
neighborhood of a pixel has 124 pixels (excluding itself), the 50%
will be 62, and if you set upd a Majority threshold of 5, that means
that the filter will require 67 or more neighbor pixels to be ON in
order to switch the current OFF pixel to ON. The default value is 1.

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::GetMaximumNumberOfIterations "

Maximum number of iterations. This filter is executed iteratively as
long as at least one pixel has changed in a previous iteration, or
until the maximum number of iterations has been reached.

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::GetRadius "

Get the radius of the neighborhood used to compute the median

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::SetBackgroundValue "

Set the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::SetForegroundValue "

Set the value associated with the Foreground (or the object) on the
binary input image and the Background .

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::SetMajorityThreshold "

Majority threshold. It is the number of pixels over 50% that will
decide whether an OFF pixel will become ON or not. For example, if the
neighborhood of a pixel has 124 pixels (excluding itself), the 50%
will be 62, and if you set upd a Majority threshold of 5, that means
that the filter will require 67 or more neighbor pixels to be ON in
order to switch the current OFF pixel to ON. The default value is 1.

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::SetMaximumNumberOfIterations "

Maximum number of iterations. This filter is executed iteratively as
long as at least one pixel has changed in a previous iteration, or
until the maximum number of iterations has been reached.

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::SetRadius "

Set the radius of the neighborhood used to compute the median.

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::SetRadius "

Set the values of the Radius vector all to value

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::VotingBinaryIterativeHoleFillingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::VotingBinaryIterativeHoleFillingImageFilter::~VotingBinaryIterativeHoleFillingImageFilter "

Destructor

";


%feature("docstring") itk::simple::WarpImageFilter "

Warps an image using an input displacement field.


WarpImageFilter warps an existing image with respect to a given displacement field.

A displacement field is represented as a image whose pixel type is
some vector type with at least N elements, where N is the dimension of
the input image. The vector type must support element access via
operator [].

The output image is produced by inverse mapping: the output pixels are
mapped back onto the input image. This scheme avoids the creation of
any holes and overlaps in the output image.

Each vector in the displacement field represent the distance between a
geometric point in the input space and a point in the output space
such that:

\\\\[ p_{in} = p_{out} + d \\\\]

Typically the mapped position does not correspond to an integer pixel
position in the input image. Interpolation via an image function is
used to compute values at non-integer positions. The default
interpolation typed used is the LinearInterpolateImageFunction . The user can specify a particular interpolation function via SetInterpolator() . Note that the input interpolator must derive from base class InterpolateImageFunction .

Position mapped to outside of the input image buffer are assigned a
edge padding value.

The LargetPossibleRegion for the output is inherited from the input
displacement field. The output image spacing, origin and orientation
may be set via SetOutputSpacing, SetOutputOrigin and
SetOutputDirection. The default are respectively a vector of 1's, a
vector of 0's and an identity matrix.

This class is templated over the type of the input image, the type of
the output image and the type of the displacement field.

The input image is set via SetInput. The input displacement field is
set via SetDisplacementField.

This filter is implemented as a multithreaded filter.


WARNING:
This filter assumes that the input type, output type and displacement
field type all have the same number of dimensions.

See:
 itk::simple::Warp for the procedural interface

 itk::WarpImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkWarpImageFilter.h
";

%feature("docstring")  itk::simple::WarpImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::WarpImageFilter::GetEdgePaddingValue "

Get the edge padding value

";

%feature("docstring")  itk::simple::WarpImageFilter::GetInterpolator "

Get/Set the interpolator function.

";

%feature("docstring")  itk::simple::WarpImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::WarpImageFilter::GetOutputDirection "

Set/Get the direction (orientation) of the output image

";

%feature("docstring")  itk::simple::WarpImageFilter::GetOutputOrigin "

Get the output image origin.

";

%feature("docstring")  itk::simple::WarpImageFilter::GetOutputSize "

Get the size of the output image.

";

%feature("docstring")  itk::simple::WarpImageFilter::GetOutputSpacing "

Get the output image spacing.

";

%feature("docstring")  itk::simple::WarpImageFilter::SetEdgePaddingValue "

Set the edge padding value

";

%feature("docstring")  itk::simple::WarpImageFilter::SetInterpolator "

Get/Set the interpolator function.

";

%feature("docstring")  itk::simple::WarpImageFilter::SetOutputDirection "

Set/Get the direction (orientation) of the output image

";

%feature("docstring")  itk::simple::WarpImageFilter::SetOutputOrigin "

Set the output image origin.

";

%feature("docstring")  itk::simple::WarpImageFilter::SetOutputParameteresFromImage "

This methods sets the output size, origin, spacing and direction to
that of the provided image

";

%feature("docstring")  itk::simple::WarpImageFilter::SetOutputSize "

Set the size of the output image.

";

%feature("docstring")  itk::simple::WarpImageFilter::SetOutputSpacing "

Set the output image spacing.

";

%feature("docstring")  itk::simple::WarpImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::WarpImageFilter::WarpImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::WarpImageFilter::~WarpImageFilter "

Destructor

";


%feature("docstring") itk::simple::WhiteTopHatImageFilter "

White top hat extracts local maxima that are larger than the
structuring element.


Top-hats are described in Chapter 4.5 of Pierre Soille's book
\"Morphological Image Analysis: Principles and Applications\", Second
Edition, Springer, 2003.


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 itk::simple::WhiteTopHat for the procedural interface

 itk::WhiteTopHatImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkWhiteTopHatImageFilter.h
";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::GetKernelRadius "

Get the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::GetKernelType "

Get the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::GetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::SafeBorderOff "
";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::SafeBorderOn "

Set the value of SafeBorder to true or false respectfully.

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::SetKernelRadius "

Set the radius of the kernel structuring element.

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::SetKernelRadius "

Set the values of the KernelRadius vector all to value

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::SetKernelType "

Set the kernel or structuring element used for the morphology.

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::SetSafeBorder "

A safe border is added to input image to avoid borders effects and
remove it once the closing is done

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::WhiteTopHatImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::WhiteTopHatImageFilter::~WhiteTopHatImageFilter "

Destructor

";


%feature("docstring") itk::simple::WienerDeconvolutionImageFilter "

The Wiener deconvolution image filter is designed to restore an image
convolved with a blurring kernel while keeping noise enhancement to a
minimum.


The Wiener filter aims to minimize noise enhancement induced by
frequencies with low signal-to-noise ratio. The Wiener filter kernel
is defined in the frequency domain as $W(\\\\omega) = H^*(\\\\omega) / (|H(\\\\omega)|^2 + (1 /
SNR(\\\\omega)))$ where $H(\\\\omega)$ is the Fourier transform of the blurring kernel with which the
original image was convolved and the signal-to-noise ratio $SNR(\\\\omega)$ . $SNR(\\\\omega)$ is defined by $P_f(\\\\omega) / P_n(\\\\omega)$ where $P_f(\\\\omega)$ is the power spectral density of the uncorrupted signal and $P_n(\\\\omega)$ is the power spectral density of the noise. When applied to the input
blurred image, this filter produces an estimate $\\\\hat{f}(x)$ of the true underlying signal $f(x)$ that minimizes the expected error between $\\\\hat{f}(x)$ and $f(x)$ .

This filter requires two inputs, the image to be deconvolved and the
blurring kernel. These two inputs can be set using the methods
SetInput() and SetKernelImage() , respectively.

The power spectral densities of the signal and noise are typically
unavailable for a given problem. In particular, $P_f(\\\\omega)$ cannot be computed from $f(x)$ because this unknown signal is precisely the signal that this filter
aims to recover. Nevertheless, it is common for the noise to have a
power spectral density that is flat or decreasing significantly more
slowly than the power spectral density of a typical image as the
frequency $\\\\omega$ increases. Hence, $P_n(\\\\omega)$ can typically be approximated with a constant, and this filter makes
this assumption (see the NoiseVariance member variable). $P_f(\\\\omega)$ , on the other hand, will vary with input. This filter computes the
power spectral density of the input blurred image, subtracts the power
spectral density of the noise, and uses the result as the estimate of $P_f(\\\\omega)$ .

For further information on the Wiener deconvolution filter, please see
\"Digital Signal Processing\" by Kenneth R. Castleman, Prentice Hall,
1995


Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France

Chris Mullins, The University of North Carolina at Chapel Hill

Cory Quammen, The University of North Carolina at Chapel Hill

See:
 itk::simple::WienerDeconvolution for the procedural interface

 itk::WienerDeconvolutionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkWienerDeconvolutionImageFilter.h
";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::GetBoundaryCondition "
";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::GetNoiseVariance "

Set/get the variance of the zero-mean Gaussian white noise assumed to
be added to the input.

";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::GetNormalize "
";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::GetOutputRegionMode "
";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::NormalizeOff "
";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::NormalizeOn "

Set the value of Normalize to true or false respectfully.

";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::SetBoundaryCondition "
";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::SetNoiseVariance "

Set/get the variance of the zero-mean Gaussian white noise assumed to
be added to the input.

";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::SetNormalize "

Normalize the output image by the sum of the kernel components

";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::SetOutputRegionMode "
";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::WienerDeconvolutionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::WienerDeconvolutionImageFilter::~WienerDeconvolutionImageFilter "

Destructor

";


%feature("docstring") itk::simple::WrapPadImageFilter "

Increase the image size by padding with replicants of the input image
value.


WrapPadImageFilter changes the image bounds of an image. Added pixels are filled in with
a wrapped replica of the input image. For instance, if the output
image needs a pixel that is two pixels to the left of the
LargestPossibleRegion of the input image, the value assigned will be
from the pixel two pixels inside the right boundary of the
LargestPossibleRegion. The image bounds of the output must be
specified.

Visual explanation of padding regions.

This filter is implemented as a multithreaded filter. It provides a
ThreadedGenerateData() method for its implementation.


See:
 MirrorPadImageFilter , ConstantPadImageFilter

 itk::simple::WrapPad for the procedural interface

 itk::WrapPadImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkWrapPadImageFilter.h
";

%feature("docstring")  itk::simple::WrapPadImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::WrapPadImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::WrapPadImageFilter::GetPadLowerBound "
";

%feature("docstring")  itk::simple::WrapPadImageFilter::GetPadUpperBound "
";

%feature("docstring")  itk::simple::WrapPadImageFilter::SetPadLowerBound "
";

%feature("docstring")  itk::simple::WrapPadImageFilter::SetPadUpperBound "
";

%feature("docstring")  itk::simple::WrapPadImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::WrapPadImageFilter::WrapPadImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::WrapPadImageFilter::~WrapPadImageFilter "

Destructor

";


%feature("docstring") itk::simple::XorImageFilter "

Computes the XOR bitwise operator pixel-wise between two images.


This class is templated over the types of the two input images and the
type of the output image. Numeric conversions (castings) are done by
the C++ defaults.

Since the bitwise XOR operation is only defined in C++ for integer
types, the images passed to this filter must comply with the
requirement of using integer pixel type.

The total operation over one pixel will be


Where \"^\" is the boolean XOR operator in C++.
See:
 itk::simple::Xor for the procedural interface

 itk::XorImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkXorImageFilter.h
";

%feature("docstring")  itk::simple::XorImageFilter::Execute "

Execute the filter on the input images

";

%feature("docstring")  itk::simple::XorImageFilter::Execute "
";

%feature("docstring")  itk::simple::XorImageFilter::Execute "

Execute the filter with an image and a constant

";

%feature("docstring")  itk::simple::XorImageFilter::Execute "
";

%feature("docstring")  itk::simple::XorImageFilter::Execute "
";

%feature("docstring")  itk::simple::XorImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::XorImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::XorImageFilter::XorImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::XorImageFilter::~XorImageFilter "

Destructor

";


%feature("docstring") itk::simple::YenThresholdImageFilter "

Threshold an image using the Yen Threshold.


This filter creates a binary thresholded image that separates an image
into foreground and background components. The filter computes the
threshold using the YenThresholdCalculator and applies that threshold to the input image using the BinaryThresholdImageFilter .


Richard Beare

Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.
 This implementation was taken from the Insight Journal paper: https://hdl.handle.net/10380/3279 or http://www.insight-journal.org/browse/publication/811


See:
 HistogramThresholdImageFilter

 itk::simple::YenThreshold for the procedural interface

 itk::YenThresholdImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkYenThresholdImageFilter.h
";

%feature("docstring")  itk::simple::YenThresholdImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::Execute "
";

%feature("docstring")  itk::simple::YenThresholdImageFilter::GetInsideValue "

Get the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::GetMaskOutput "
";

%feature("docstring")  itk::simple::YenThresholdImageFilter::GetMaskValue "
";

%feature("docstring")  itk::simple::YenThresholdImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::GetNumberOfHistogramBins "
";

%feature("docstring")  itk::simple::YenThresholdImageFilter::GetOutsideValue "

Get the \"outside\" pixel value.

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::GetThreshold "

Get the computed threshold.


This is a measurement. Its value is updated in the Execute methods, so
the value will only be valid after an execution.

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::MaskOutputOff "
";

%feature("docstring")  itk::simple::YenThresholdImageFilter::MaskOutputOn "

Set the value of MaskOutput to true or false respectfully.

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::SetInsideValue "

Set the \"inside\" pixel value.

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::SetMaskOutput "

Do you want the output to be masked by the mask used in histogram
construction. Only relevant if masking is in use.

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::SetMaskValue "

The value in the mask image, if used, indicating voxels that should be
included. Default is the max of pixel type, as in the
MaskedImageToHistogramFilter

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::SetNumberOfHistogramBins "

Set/Get the number of histogram bins.

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::SetOutsideValue "

Set the \"outside\" pixel value. The default value NumericTraits<OutputPixelType>::Zero.

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::YenThresholdImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::YenThresholdImageFilter::~YenThresholdImageFilter "

Destructor

";


%feature("docstring") itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter "

This filter implements a zero-crossing based edge detecor.


The zero-crossing based edge detector looks for pixels in the
Laplacian of an image where the value of the Laplacian passes through
zero points where the Laplacian changes sign. Such points often occur
at \"edges\" in images i.e. points where the intensity of the image
changes rapidly, but they also occur at places that are not as easy to
associate with edges. It is best to think of the zero crossing
detector as some sort of feature detector rather than as a specific
edge detector.


Zero crossings always lie on closed contours and so the output from
the zero crossing detector is usually a binary image with single pixel
thickness lines showing the positions of the zero crossing points.

In this implementation, the input image is first smoothed with a
Gaussian filter, then the LaplacianImageFilter is applied to smoothed image. Finally the zero-crossing of the
Laplacian of the smoothed image is detected. The output is a binary
image.
Inputs and Outputs
The input to the filter should be a scalar, itk::Image of arbitrary dimension. The output image is a binary, labeled image.
See itkZeroCrossingImageFilter for more information on requirements of
the data type of the output.

To use this filter, first set the parameters (variance and maximum
error) needed by the embedded DiscreteGaussianImageFilter , i.e. See DiscreteGaussianImageFilter for information about these parameters. Optionally, you may also set
foreground and background values for the zero-crossing filter. The
default label values are Zero for the background and One for the
foreground, as defined in NumericTraits for the data type of the output image.

See:
 DiscreteGaussianImageFilter

 LaplacianImageFilter

 ZeroCrossingImageFilter

 itk::simple::ZeroCrossingBasedEdgeDetection for the procedural interface

 itk::ZeroCrossingBasedEdgeDetectionImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkZeroCrossingBasedEdgeDetectionImageFilter.h
";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::GetBackgroundValue "

Get/Set the label values for the ZeroCrossingImageFilter

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::GetForegroundValue "

Get/Set the label values for the ZeroCrossingImageFilter

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::GetMaximumError "

Standard get/set macros for Gaussian filter parameters.

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::GetVariance "

Standard get/set macros for Gaussian filter parameters.

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::SetBackgroundValue "

Get/Set the label values for the ZeroCrossingImageFilter

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::SetForegroundValue "

Get/Set the label values for the ZeroCrossingImageFilter

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::SetMaximumError "

Set the MaximumError parameter needed by the embedded gaussian filter
This value is used to set the desired maximum error of the gaussian
approximation. Maximum error is the difference between the area under
the discrete Gaussian curve and the area under the continuous
Gaussian. Maximum error affects the Gaussian operator size. The value
must be between 0.0 and 1.0.

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::SetVariance "

Set the variance parameter needed by the embedded gaussian filter

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::ZeroCrossingBasedEdgeDetectionImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ZeroCrossingBasedEdgeDetectionImageFilter::~ZeroCrossingBasedEdgeDetectionImageFilter "

Destructor

";


%feature("docstring") itk::simple::ZeroCrossingImageFilter "

This filter finds the closest pixel to the zero-crossings (sign
changes) in a signed itk::Image .


Pixels closest to zero-crossings are labeled with a foreground value.
All other pixels are marked with a background value. The algorithm
works by detecting differences in sign among neighbors using city-
block style connectivity (4-neighbors in 2d, 6-neighbors in 3d, etc.).

Inputs and Outputs
The input to this filter is an itk::Image of arbitrary dimension. The algorithm assumes a signed data type
(zero-crossings are not defined for unsigned data types), and requires
that operator>, operator<, operator==, and operator!= are defined.

The output of the filter is a binary, labeled image of user-specified
type. By default, zero-crossing pixels are labeled with a default
\"foreground\" value of itk::NumericTraits<OutputDataType>::OneValue() , where OutputDataType is the data type of the output image. All
other pixels are labeled with a default \"background\" value of itk::NumericTraits<OutputDataType>::ZeroValue() .
Parameters
There are two parameters for this filter. ForegroundValue is the value
that marks zero-crossing pixels. The BackgroundValue is the value
given to all other pixels.

See:
 Image

 Neighborhood

 NeighborhoodOperator

 NeighborhoodIterator

 itk::simple::ZeroCrossing for the procedural interface

 itk::ZeroCrossingImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkZeroCrossingImageFilter.h
";

%feature("docstring")  itk::simple::ZeroCrossingImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ZeroCrossingImageFilter::GetBackgroundValue "

Set/Get the label value for non-zero-crossing pixels.

";

%feature("docstring")  itk::simple::ZeroCrossingImageFilter::GetForegroundValue "

Set/Get the label value for zero-crossing pixels.

";

%feature("docstring")  itk::simple::ZeroCrossingImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ZeroCrossingImageFilter::SetBackgroundValue "

Set/Get the label value for non-zero-crossing pixels.

";

%feature("docstring")  itk::simple::ZeroCrossingImageFilter::SetForegroundValue "

Set/Get the label value for zero-crossing pixels.

";

%feature("docstring")  itk::simple::ZeroCrossingImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ZeroCrossingImageFilter::ZeroCrossingImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ZeroCrossingImageFilter::~ZeroCrossingImageFilter "

Destructor

";


%feature("docstring") itk::simple::ZeroFluxNeumannPadImageFilter "

Increase the image size by padding according to the zero-flux Neumann
boundary condition.


A filter which extends the image size and fill the missing pixels
according to a Neumann boundary condition where first, upwind
derivatives on the boundary are zero. This is a useful condition in
solving some classes of differential equations.

For example, invoking this filter on an image with a corner like: returns the following padded image:


Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA
de Jouy-en-Josas, France.

See:
 WrapPadImageFilter , MirrorPadImageFilter , ConstantPadImageFilter , ZeroFluxNeumannBoundaryCondition

 itk::simple::ZeroFluxNeumannPad for the procedural interface

 itk::ZeroFluxNeumannPadImageFilter for the Doxygen on the original ITK class.


C++ includes: sitkZeroFluxNeumannPadImageFilter.h
";

%feature("docstring")  itk::simple::ZeroFluxNeumannPadImageFilter::Execute "

Execute the filter on the input image

";

%feature("docstring")  itk::simple::ZeroFluxNeumannPadImageFilter::GetName "

Name of this class

";

%feature("docstring")  itk::simple::ZeroFluxNeumannPadImageFilter::GetPadLowerBound "
";

%feature("docstring")  itk::simple::ZeroFluxNeumannPadImageFilter::GetPadUpperBound "
";

%feature("docstring")  itk::simple::ZeroFluxNeumannPadImageFilter::SetPadLowerBound "
";

%feature("docstring")  itk::simple::ZeroFluxNeumannPadImageFilter::SetPadUpperBound "
";

%feature("docstring")  itk::simple::ZeroFluxNeumannPadImageFilter::ToString "

Print ourselves out

";

%feature("docstring")  itk::simple::ZeroFluxNeumannPadImageFilter::ZeroFluxNeumannPadImageFilter "

Default Constructor that takes no arguments and initializes default
parameters

";

%feature("docstring")  itk::simple::ZeroFluxNeumannPadImageFilter::~ZeroFluxNeumannPadImageFilter "

Destructor

";


%feature("docstring") itk::simple::DualMemberFunctionFactory "

A class used to instantiate and generate function objects of templated
member functions with two template arguments.




Parameters:

TMemberFunctionPointer:
is the type of pointer to member function

 Example member function and pointer:

The provided Addressor will instantiate the templeted member functions
by taking the address in the RegisterMethods. Later they can be
retrieve with the GetMemberFunction method, which returns a function
object with the same arguments as the templated member function
pointer.

An instance of a MemberFunctionFactory is bound to a specific instance of an object, so that the returned
function object does not need to have the calling object specified.


WARNING:
Use this class with caution because it can instantiate a combinatorial
number of methods.

See:
 MemberFunctionFactory


C++ includes: sitkDualMemberFunctionFactory.h
";

%feature("docstring")  itk::simple::DualMemberFunctionFactory::DualMemberFunctionFactory "

Constructor which permanently binds the constructed object to pObject.

";

%feature("docstring")  itk::simple::DualMemberFunctionFactory::GetMemberFunction "

Returns a function object for the combination of PixelID1 and
PixelID2, and image dimension.


pixelID1 or pixelID2 is the value of Image::GetPixelIDValue(), or PixelIDToPixelIDValue<PixelIDType>::Result

imageDimension is the value returned by Image::GetDimension()

Example usage:

If the requested member function is not registered then an exception
is generated. The returned function object is guaranteed to be valid.

";

%feature("docstring")  itk::simple::DualMemberFunctionFactory::HasMemberFunction "

Query to determine if an member function has been registered for
pixelID1, pixelID2 and imageDimension.

";

%feature("docstring")  itk::simple::DualMemberFunctionFactory::Register "

Registers a specific member function.


Registers a member function templated over TImageType1 and TImageType2

";


%feature("docstring") itk::simple::MemberFunctionFactory "

A class used to instantiate and generate function object to templated
member functions.




Parameters:

TMemberFunctionPointer:
is the type of pointer to member function

 Example member function pointer:

The RegisterMemberFunctions instantiate the templeted member functions
and registers the member function pointer, so that it be used for
dispatch later. Later they can be retrieve with the GetMemberFunction
methods, which return a function object with the same arguments as the
templated member function pointer.

An instance of a MemberFunctionFactory is bound to a specific instance of an object, so that the returned
function object does not need to have the calling object specified.

C++ includes: sitkMemberFunctionFactory.h
";

%feature("docstring")  itk::simple::MemberFunctionFactory::GetMemberFunction "

Returns a function object for the PixelIndex, and image dimension.


pixelID is the value of Image::GetPixelIDValue(), or PixelIDToPixelIDValue<PixelIDType>::Result

imageDimension is the value returned by Image::GetDimension()

Example usage:

If the requested member function is not registered then an exception
is generated. The returned function object is guaranteed to be valid.

";

%feature("docstring")  itk::simple::MemberFunctionFactory::HasMemberFunction "

Query to determine if an member function has been registered for
pixelID and imageDimension.

";

%feature("docstring")  itk::simple::MemberFunctionFactory::MemberFunctionFactory "

Constructor which permanently binds the constructed object to pObject.

";

%feature("docstring")  itk::simple::MemberFunctionFactory::Register "

Registers a specific member function.


Registers a member function which will be dispatched to the TImageType
type

";


%feature("docstring") itk::simple::MemberFunctionFactoryBase "

A base class for the MemberFunctionFactory.


This class is for specialization needed for different arity for the
templated member function pointer

C++ includes: sitkMemberFunctionFactoryBase.h
";


%feature("docstring") itk::simple::MemberFunctionFactoryBase< TMemberFunctionPointer, TKey, 0 > "
C++ includes: sitkMemberFunctionFactoryBase.h
";


%feature("docstring") itk::simple::MemberFunctionFactoryBase< TMemberFunctionPointer, TKey, 1 > "
C++ includes: sitkMemberFunctionFactoryBase.h
";


%feature("docstring") itk::simple::MemberFunctionFactoryBase< TMemberFunctionPointer, TKey, 2 > "
C++ includes: sitkMemberFunctionFactoryBase.h
";


%feature("docstring") itk::simple::MemberFunctionFactoryBase< TMemberFunctionPointer, TKey, 3 > "
C++ includes: sitkMemberFunctionFactoryBase.h
";


%feature("docstring") itk::simple::MemberFunctionFactoryBase< TMemberFunctionPointer, TKey, 4 > "
C++ includes: sitkMemberFunctionFactoryBase.h
";


%feature("docstring") itk::simple::MemberFunctionFactoryBase< TMemberFunctionPointer, TKey, 5 > "
C++ includes: sitkMemberFunctionFactoryBase.h
";

%feature("docstring")  itk::Accessor::BioRadImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::BMPImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::BoxAccumulateFunction "
";

%feature("docstring")  itk::Accessor::BoxMeanCalculatorFunction "
";

%feature("docstring")  itk::Accessor::BoxSigmaCalculatorFunction "
";

%feature("docstring")  itk::Accessor::BoxSquareAccumulateFunction "
";

%feature("docstring")  itk::Accessor::Bruker2dseqImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::ClearContent "
";

%feature("docstring")  itk::Accessor::CompareDCMTKFileReaders "
";

%feature("docstring")  itk::Accessor::CompensatedSummationAddElement "
";

%feature("docstring")  itk::Accessor::CompensatedSummationAddElement "
";

%feature("docstring")  itk::Accessor::ComputeStartEnd "
";

%feature("docstring")  itk::Accessor::CopyLineToImage "
";

%feature("docstring")  itk::Accessor::CopyMeshToMesh "
";

%feature("docstring")  itk::Accessor::CopyMeshToMeshCellData "
";

%feature("docstring")  itk::Accessor::CopyMeshToMeshCells "
";

%feature("docstring")  itk::Accessor::CopyMeshToMeshEdgeCells "
";

%feature("docstring")  itk::Accessor::CopyMeshToMeshPointData "
";

%feature("docstring")  itk::Accessor::CopyMeshToMeshPoints "
";

%feature("docstring")  itk::Accessor::CornerOffsets "
";

%feature("docstring")  itk::Accessor::CrossProduct "
";

%feature("docstring")  itk::Accessor::CrossProduct "
";

%feature("docstring")  itk::Accessor::CrossProduct "
";

%feature("docstring")  itk::Accessor::CrossProduct "
";

%feature("docstring")  itk::Accessor::CrossProduct "
";

%feature("docstring")  itk::Accessor::CrossProduct "
";

%feature("docstring")  itk::Accessor::DoAnchorFace "
";

%feature("docstring")  itk::Accessor::DoFace "
";

%feature("docstring")  itk::Accessor::EncapsulateMetaData "
";

%feature("docstring")  itk::Accessor::EncapsulateMetaData "
";

%feature("docstring")  itk::Accessor::ExposeMetaData "
";

%feature("docstring")  itk::Accessor::FillForwardExt "
";

%feature("docstring")  itk::Accessor::FillLineBuffer "
";

%feature("docstring")  itk::Accessor::FillReverseExt "
";

%feature("docstring")  itk::Accessor::GDCMImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::GE4ImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::GE5ImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::GenerateConnectedImageNeighborhoodShapeOffsets "
";

%feature("docstring")  itk::Accessor::GenerateImageNeighborhoodOffsets "
";

%feature("docstring")  itk::Accessor::GenerateRectangularImageNeighborhoodOffsets "
";

%feature("docstring")  itk::Accessor::Get64BitPragma "
";

%feature("docstring")  itk::Accessor::GetLinePixels "
";

%feature("docstring")  itk::Accessor::GetPixelDimension "
";

%feature("docstring")  itk::Accessor::GetTransformName "
";

%feature("docstring")  itk::Accessor::GetTypename "
";

%feature("docstring")  itk::Accessor::GetTypenameInString "
";

%feature("docstring")  itk::Accessor::GetValidTypename "
";

%feature("docstring")  itk::Accessor::GiplImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::HDF5ImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::HDF5TransformIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::IMAGEIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::Int2EigenValueOrderEnum "
";

%feature("docstring")  itk::Accessor::IsGPUAvailable "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_FIXEDARRAY_TYPE "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_FIXEDARRAY_TYPE "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_FIXEDARRAY_TYPE "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_FIXEDARRAY_TYPE "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_DEFAULTCONVERTTRAITS_NATIVE_SPECIAL "
";

%feature("docstring")  itk::Accessor::ITK_MESH_DEFAULTCONVERTTRAITS_ARRAY_TYPE_ALL_TYPES_MACRO "
";

%feature("docstring")  itk::Accessor::ITK_MESH_DEFAULTCONVERTTRAITS_ARRAY_TYPE_ALL_TYPES_MACRO "
";

%feature("docstring")  itk::Accessor::ITK_MESH_DEFAULTCONVERTTRAITS_COMPLEX_TYPE "
";

%feature("docstring")  itk::Accessor::ITK_MESH_DEFAULTCONVERTTRAITS_COMPLEX_TYPE "
";

%feature("docstring")  itk::Accessor::ITK_MESH_DEFAULTCONVERTTRAITS_FIXEDARRAY_TYPE_ALL_TYPES_MACRO "
";

%feature("docstring")  itk::Accessor::ITK_MESH_DEFAULTCONVERTTRAITS_FIXEDARRAY_TYPE_ALL_TYPES_MACRO "
";

%feature("docstring")  itk::Accessor::ITK_MESH_DEFAULTCONVERTTRAITS_FIXEDARRAY_TYPE_ALL_TYPES_MACRO "
";

%feature("docstring")  itk::Accessor::ITK_MESH_DEFAULTCONVERTTRAITS_FIXEDARRAY_TYPE_ALL_TYPES_MACRO "
";

%feature("docstring")  itk::Accessor::ITK_MESH_DEFAULTCONVERTTRAITS_MATRIX_TYPE_ALL_TYPES_MACRO "
";

%feature("docstring")  itk::Accessor::itkEventMacroDeclaration "
";

%feature("docstring")  itk::Accessor::itkEventMacroDeclaration "
";

%feature("docstring")  itk::Accessor::itkEventMacroDeclaration "
";

%feature("docstring")  itk::Accessor::itkEventMacroDeclaration "
";

%feature("docstring")  itk::Accessor::itkEventMacroDeclaration "
";

%feature("docstring")  itk::Accessor::itkEventMacroDeclaration "
";

%feature("docstring")  itk::Accessor::itkEventMacroDeclaration "
";

%feature("docstring")  itk::Accessor::itkEventMacroDeclaration "
";

%feature("docstring")  itk::Accessor::itkEventMacroDeclaration "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::itkGPUKernelClassMacro "
";

%feature("docstring")  itk::Accessor::JPEG2000ImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::JPEGImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::LSMImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::MakeChainCodeTracePath "
";

%feature("docstring")  itk::Accessor::MakeEnlargedFace "
";

%feature("docstring")  itk::Accessor::MakeFourierSeriesPathTraceChainCode "
";

%feature("docstring")  itk::Accessor::MakeImageBufferRange "
";

%feature("docstring")  itk::Accessor::MatlabTransformIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MESHIOBASE_TYPEMAP "
";

%feature("docstring")  itk::Accessor::MetaImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::MINCImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::MRCImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::MvtSunf "
";

%feature("docstring")  itk::Accessor::NeedToDoFace "
";

%feature("docstring")  itk::Accessor::NiftiImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::NrrdImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::OpenCLCheckError "
";

%feature("docstring")  itk::Accessor::OpenCLGetAvailableDevices "
";

%feature("docstring")  itk::Accessor::OpenCLGetLocalBlockSize "
";

%feature("docstring")  itk::Accessor::OpenCLGetMaxFlopsDev "
";

%feature("docstring")  itk::Accessor::OpenCLPrintDeviceInfo "
";

%feature("docstring")  itk::Accessor::OpenCLSelectPlatform "
";

%feature("docstring")  itk::Accessor::PNGImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::ReadRawBytesAfterSwapping "
";

%feature("docstring")  itk::Accessor::setConnectivity "
";

%feature("docstring")  itk::Accessor::setConnectivityLater "
";

%feature("docstring")  itk::Accessor::setConnectivityPrevious "
";

%feature("docstring")  itk::Accessor::Singleton "
";

%feature("docstring")  itk::Accessor::StimulateImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::swap "
";

%feature("docstring")  itk::Accessor::TIFFImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::TransferAutoPointer "
";

%feature("docstring")  itk::Accessor::TxtTransformIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::VTKImageIOFactoryRegister__Private "
";

%feature("docstring")  itk::Accessor::WriteRawBytesAfterSwapping "
";

%feature("docstring")  itk::simple::AbsoluteValueDifference "
";

%feature("docstring")  itk::simple::AbsoluteValueDifference "
";

%feature("docstring")  itk::simple::AbsoluteValueDifference "
";

%feature("docstring")  itk::simple::Add "
";

%feature("docstring")  itk::simple::Add "
";

%feature("docstring")  itk::simple::Add "
";

%feature("docstring")  itk::simple::And "
";

%feature("docstring")  itk::simple::And "
";

%feature("docstring")  itk::simple::And "
";

%feature("docstring")  itk::simple::Atan2 "
";

%feature("docstring")  itk::simple::Atan2 "
";

%feature("docstring")  itk::simple::Atan2 "
";

%feature("docstring")  itk::simple::BSplineTransformInitializer "

BSplineTransformInitializerFilter is a helper class intended to initialize the control point grid such
that it has a physically consistent definition. It sets the transform
domain origin, physical dimensions and direction from information
obtained from the image. It also sets the mesh size if asked to do so
by calling SetTransformDomainMeshSize()before calling
InitializeTransform().


This function directly calls the execute method of BSplineTransformInitializerFilter in order to support a procedural API


See:
 itk::simple::BSplineTransformInitializerFilter for the object oriented interface


";

%feature("docstring")  itk::simple::Cast "
";

%feature("docstring")  itk::simple::CenteredTransformInitializer "

CenteredTransformInitializer is a helper class intended to initialize the center of rotation and
the translation of Transforms having the center of rotation among
their parameters.


This function directly calls the execute method of CenteredTransformInitializerFilter in order to support a procedural API


See:
 itk::simple::CenteredTransformInitializerFilter for the object oriented interface


";

%feature("docstring")  itk::simple::CenteredVersorTransformInitializer "

CenteredVersorTransformInitializer is a helper class intended to initialize the center of rotation,
versor, and translation of the VersorRigid3DTransform.


This function directly calls the execute method of
CenteredVectorTransformInitializerFilter in order to support a
procedural API.


See:
 itk::simple::CenteredVersorTransformInitializerFilter for the object oriented interface


";

%feature("docstring")  itk::simple::CreateKernel "
";

%feature("docstring")  itk::simple::DiscreteGaussian "

Blurs an image by separable convolution with discrete gaussian
kernels. This filter performs Gaussian blurring by separable
convolution of an image and a discrete Gaussian operator (kernel).


This function directly calls the execute method of DiscreteGaussianImageFilter in order to support a procedural API


See:
 itk::simple::DiscreteGaussianImageFilter for the object oriented interface


";

%feature("docstring")  itk::simple::Divide "
";

%feature("docstring")  itk::simple::Divide "
";

%feature("docstring")  itk::simple::Divide "
";

%feature("docstring")  itk::simple::DivideFloor "
";

%feature("docstring")  itk::simple::DivideFloor "
";

%feature("docstring")  itk::simple::DivideFloor "
";

%feature("docstring")  itk::simple::DivideReal "
";

%feature("docstring")  itk::simple::DivideReal "
";

%feature("docstring")  itk::simple::DivideReal "
";

%feature("docstring")  itk::simple::Equal "
";

%feature("docstring")  itk::simple::Equal "
";

%feature("docstring")  itk::simple::Equal "
";

%feature("docstring")  itk::simple::GaborSource "

Generate an n-dimensional image of a Gabor filter.


This function directly calls the execute method of GaborImageSource in order to support a procedural API


See:
 itk::simple::GaborImageSource for the object oriented interface


";

%feature("docstring")  itk::simple::GaussianSource "

Generate an n-dimensional image of a Gaussian.


This function directly calls the execute method of GaussianImageSource in order to support a procedural API


See:
 itk::simple::GaussianImageSource for the object oriented interface


";

%feature("docstring")  itk::simple::GetImageFromVectorImage "

A utility method to help convert between itk image types efficiently.

";

%feature("docstring")  itk::simple::GetPixelIDValueAsString "
";

%feature("docstring")  itk::simple::GetPixelIDValueAsString "
";

%feature("docstring")  itk::simple::GetPixelIDValueFromString "

Function mapping enumeration names in std::string to values.


This function is intended for use by the R bindings. R stores the
enumeration values using the names : \"sitkUnkown\", \"sitkUInt8\",
etc from PixelIDValueEnum above. This function is used to provide the
integer values using calls like:

val = GetPixelIDValueFromString(\"sitkInt32\")

If the pixel type has not been instantiated then the sitkUnknown value
(-1) will be returned. If the pixel type string is not recognized
(i.e. is not in the set of tested names) then the return value is -99.
The idea is to provide a warning (via the R package) if this function
needs to be updated to match changes to PixelIDValueEnum - i.e. if a
new pixel type is added.

";

%feature("docstring")  itk::simple::GetVectorImageFromImage "
";

%feature("docstring")  itk::simple::GetVectorImageFromImage "
";

%feature("docstring")  itk::simple::Greater "
";

%feature("docstring")  itk::simple::Greater "
";

%feature("docstring")  itk::simple::Greater "
";

%feature("docstring")  itk::simple::GreaterEqual "
";

%feature("docstring")  itk::simple::GreaterEqual "
";

%feature("docstring")  itk::simple::GreaterEqual "
";

%feature("docstring")  itk::simple::GridSource "

Generate an n-dimensional image of a grid.


This function directly calls the execute method of GridImageSource in order to support a procedural API


See:
 itk::simple::GridImageSource for the object oriented interface


";

%feature("docstring")  itk::simple::Hash "
";

%feature("docstring")  itk::simple::ImportAsDouble "
";

%feature("docstring")  itk::simple::ImportAsFloat "
";

%feature("docstring")  itk::simple::ImportAsInt16 "
";

%feature("docstring")  itk::simple::ImportAsInt32 "
";

%feature("docstring")  itk::simple::ImportAsInt64 "
";

%feature("docstring")  itk::simple::ImportAsInt8 "
";

%feature("docstring")  itk::simple::ImportAsUInt16 "
";

%feature("docstring")  itk::simple::ImportAsUInt32 "
";

%feature("docstring")  itk::simple::ImportAsUInt64 "
";

%feature("docstring")  itk::simple::ImportAsUInt8 "
";

%feature("docstring")  itk::simple::LandmarkBasedTransformInitializer "

itk::simple::LandmarkBasedTransformInitializerFilter Procedural Interface


This function directly calls the execute method of LandmarkBasedTransformInitializerFilter in order to support a procedural API


See:
 itk::simple::LandmarkBasedTransformInitializerFilter for the object oriented interface


";

%feature("docstring")  itk::simple::Less "
";

%feature("docstring")  itk::simple::Less "
";

%feature("docstring")  itk::simple::Less "
";

%feature("docstring")  itk::simple::LessEqual "
";

%feature("docstring")  itk::simple::LessEqual "
";

%feature("docstring")  itk::simple::LessEqual "
";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplex "
";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplex "
";

%feature("docstring")  itk::simple::MagnitudeAndPhaseToComplex "
";

%feature("docstring")  itk::simple::make_scope_exit "
";

%feature("docstring")  itk::simple::Maximum "
";

%feature("docstring")  itk::simple::Maximum "
";

%feature("docstring")  itk::simple::Maximum "
";

%feature("docstring")  itk::simple::Minimum "
";

%feature("docstring")  itk::simple::Minimum "
";

%feature("docstring")  itk::simple::Minimum "
";

%feature("docstring")  itk::simple::Modulus "
";

%feature("docstring")  itk::simple::Modulus "
";

%feature("docstring")  itk::simple::Modulus "
";

%feature("docstring")  itk::simple::Multiply "
";

%feature("docstring")  itk::simple::Multiply "
";

%feature("docstring")  itk::simple::Multiply "
";

%feature("docstring")  itk::simple::NotEqual "
";

%feature("docstring")  itk::simple::NotEqual "
";

%feature("docstring")  itk::simple::NotEqual "
";

%feature("docstring")  itk::simple::Or "
";

%feature("docstring")  itk::simple::Or "
";

%feature("docstring")  itk::simple::Or "
";

%feature("docstring")  itk::simple::PatchBasedDenoising "

itk::simple::PatchBasedDenoisingImageFilter Procedural Interface


This function directly calls the execute method of PatchBasedDenoisingImageFilter in order to support a procedural API


See:
 itk::simple::PatchBasedDenoisingImageFilter for the object oriented interface


";

%feature("docstring")  itk::simple::PatchBasedDenoising "
";

%feature("docstring")  itk::simple::PhysicalPointSource "

Generate an image of the physical locations of each pixel.


This function directly calls the execute method of PhysicalPointImageSource in order to support a procedural API


See:
 itk::simple::PhysicalPointImageSource for the object oriented interface


";

%feature("docstring")  itk::simple::Pow "
";

%feature("docstring")  itk::simple::Pow "
";

%feature("docstring")  itk::simple::Pow "
";

%feature("docstring")  itk::simple::ReadImage "

ReadImage is a procedural interface to the ImageSeriesReader class which is convenient for most image reading tasks.




Parameters:

fileNames:
a vector of file names

outputPixelType:
see ImageReaderBase::SetOutputPixelType

imageIO:
see ImageReaderBase::SetImageIO


When reading a series of images that have meta-data associated with
them (e.g. a DICOM series) the resulting image will have an empty
meta-data dictionary. If you need the meta-data dictionaries
associated with each slice then you should use the ImageSeriesReader class.

See:
 itk::simple::ImageFileReader for reading a single file.

 itk::simple::ImageSeriesReader for reading a series and meta-data dictionaries.


";

%feature("docstring")  itk::simple::ReadImage "

ReadImage is a procedural interface to the ImageFileReader class which is convenient for most image reading tasks.




Parameters:

filename:
the filename of an Image e.g. \"cthead.mha\"

outputPixelType:
see ImageReaderBase::SetOutputPixelType

imageIO:
see ImageReaderBase::SetImageIO


See:
 itk::simple::ImageFileReader for reading a single file.

 itk::simple::ImageSeriesReader for reading a series and meta-data dictionaries.


";

%feature("docstring")  itk::simple::ReadTransform "
";

%feature("docstring")  itk::simple::Show "

Display an image in an external viewer (Fiji by default)

This function directly calls the execute method of ImageViewer in order to support a procedural API

";

%feature("docstring")  itk::simple::sitkITKDirectionToSTL "
";

%feature("docstring")  itk::simple::sitkITKImageRegionToSTL "

Convert an ITK ImageRegion to and std::vector with the first part being the start index followed
by the size.

";

%feature("docstring")  itk::simple::sitkITKVectorToSTL "

Convert an ITK fixed width vector to a std::vector.

";

%feature("docstring")  itk::simple::sitkITKVectorToSTL "
";

%feature("docstring")  itk::simple::sitkITKVersorToSTL "
";

%feature("docstring")  itk::simple::sitkSTLToITKDirection "
";

%feature("docstring")  itk::simple::sitkSTLVectorToITK "

Copy the elements of an std::vector into an ITK fixed width vector.


If there are more elements in parameter \"in\" than the templated ITK
vector type, they are truncated. If less, then an exception is
generated.

";

%feature("docstring")  itk::simple::sitkSTLVectorToITKPointVector "
";

%feature("docstring")  itk::simple::sitkSTLVectorToITKVersor "
";

%feature("docstring")  itk::simple::sitkVectorOfITKVectorToSTL "

Convert an ITK style array of ITK fixed witdth vector to std::vector.


An example input type is itk::FixedArray<itk::Point<3>, 3>

";

%feature("docstring")  itk::simple::SmoothingRecursiveGaussian "

Computes the smoothing of an image by convolution with the Gaussian
kernels implemented as IIR filters.


This function directly calls the execute method of SmoothingRecursiveGaussianImageFilter in order to support a procedural API


See:
 itk::simple::SmoothingRecursiveGaussianImageFilter for the object oriented interface


";

%feature("docstring")  itk::simple::SquaredDifference "
";

%feature("docstring")  itk::simple::SquaredDifference "
";

%feature("docstring")  itk::simple::SquaredDifference "
";

%feature("docstring")  itk::simple::Subtract "
";

%feature("docstring")  itk::simple::Subtract "
";

%feature("docstring")  itk::simple::Subtract "
";

%feature("docstring")  itk::simple::TransformToDisplacementField "

Generate a displacement field from a coordinate transform.


This function directly calls the execute method of TransformToDisplacementFieldFilter in order to support a procedural API


See:
 itk::simple::TransformToDisplacementFieldFilter for the object oriented interface


";

%feature("docstring")  itk::simple::Unused "

A function which does nothing.


This function is to be used to mark parameters as unused to suppress
compiler warning.

";

%feature("docstring")  itk::simple::WriteImage "

WriteImage is a procedural interface to the ImageSeriesWriter. class which is convenient for many image writing tasks.




Parameters:

image:
the input image to be written

fileNames:
a vector of filenames of length equal to the number of slices in the
image.

useCompression:
request to compress the written file

compressionLevel:
a hint for the amount of compression to be applied during writing.


See:
 itk::simple::ImageFileWriter for writing a single file.


";

%feature("docstring")  itk::simple::WriteImage "

WriteImage is a procedural interface to the ImageFileWriter. class which is convenient for many image writing tasks.




Parameters:

image:
the input image to be written

fileName:
the filename of an Image e.g. \"cthead.mha\"

useCompression:
request to compress the written file

compressionLevel:
a hint for the amount of compression to be applied during writing


See:
 itk::simple::ImageFileWriter for writing a single file.


";

%feature("docstring")  itk::simple::WriteTransform "
";

%feature("docstring")  itk::simple::Xor "
";

%feature("docstring")  itk::simple::Xor "
";

%feature("docstring")  itk::simple::Xor "
";

%feature("docstring")  itk::simple::hash_combine "

A utility function to chain hashes

";

%feature("docstring")  itk::simple::ioutils::CreateImageIOByName "
";

%feature("docstring")  itk::simple::ioutils::GetRegisteredImageIOs "
";

%feature("docstring")  itk::simple::ioutils::PrintRegisteredImageIOs "
";


%feature("docstring") itk::simple::BasicPixelID "

This type is used as an identity for pixel of itk::Image type

This is an empty type which is used for compile-time meta-programming.
It does not contain any information, an image type can be converted to
one of the PixelID types, and an PixelID can be converted to a value.
However, a run-time value can not be converted to this compile time
type.


See:
 PixelIDToImageType

 ImageTypeToPixelID

 ImageTypeToPixelIDValue

 PixelIDToPixelIDValue


C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::ConditionalValue "
C++ includes: sitkConditional.h
";


%feature("docstring") itk::simple::ImageTypeToPixelID "

A meta-programming tool to query the PixelID property of an \"itk
image type\" at compile type

This structure contains one property,
ImageTypeToPixelID<T>::PixelIDType is the \"itk image type\" of the
pixel ID.


See:
 BasicPixelID

 VectorPixelID

 LabelPixelID

 ImageTypeToPixelIDValue


C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::ImageTypeToPixelIDValue "
C++ includes: sitkPixelIDValues.h
";


%feature("docstring") itk::simple::ImageTypeToPixelIDValue< itk::ImageBase< VImageDimension > > "
C++ includes: sitkPixelIDValues.h
";


%feature("docstring") itk::simple::ImageTypeToPixelID< itk::Image< TPixelType, VImageDimension > > "
C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::ImageTypeToPixelID< itk::LabelMap< itk::LabelObject< TLabelType, VImageDimension > > > "
C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::ImageTypeToPixelID< itk::VectorImage< TPixelType, VImageDimension > > "
C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::IsBasic "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsBasic< BasicPixelID< TPixelType > > "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsBasic< itk::Image< TPixelType, VImageDimension > > "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsInstantiated "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsInstantiated< itk::Image< TPixelType, VImageDimension >, 0 > "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsInstantiated< itk::LabelMap< itk::LabelObject< TLabelType, VImageDimension > >, 0 > "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsInstantiated< itk::VectorImage< TPixelType, VImageDimension >, 0 > "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsLabel "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsLabel< LabelPixelID< TPixelType > > "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsLabel< itk::LabelMap< itk::LabelObject< TLabelType, VImageDimension > > > "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsVector "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsVector< VectorPixelID< TPixelType > > "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::IsVector< itk::VectorImage< TPixelType, VImageDimension > > "
C++ includes: sitkPixelIDTokens.h
";


%feature("docstring") itk::simple::LabelPixelID "

This type is used as an identity for pixel of itk::LabelMap type

This is an empty type which is used for compile-time meta-programming.
It does not contain any information, an image type can be converted to
one of the PixelID types, and an PixelID can be converted to a value.
However, a run-time value can not be converted to this compile time
type.


See:
 PixelIDToImageType

 ImageTypeToPixelID

 ImageTypeToPixelIDValue

 PixelIDToPixelIDValue


C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::PixelIDToImageType "

A meta-programming tool to query the \"itk image type\" if a PixelID
at compile type

This structure contains one property, PixelIDToImageType<T>::ImageType
is the \"itk image type\" of the pixel ID.


See:
 BasicPixelID

 VectorPixelID

 LabelPixelID

 ImageTypeToPixelIDValue


C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::PixelIDToImageType< BasicPixelID< TPixelType >, VImageDimension > "
C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::PixelIDToImageType< LabelPixelID< TLabelType >, VImageDimension > "
C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::PixelIDToImageType< VectorPixelID< TVectorPixelType >, VImageDimension > "
C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::PixelIDToPixelIDValue "
C++ includes: sitkPixelIDValues.h
";


%feature("docstring") itk::simple::VectorPixelID "

This type is used as an identity for pixel of itk::VectorImage type

This is an empty type which is used for compile-time meta-programming.
It does not contain any information, an image type can be converted to
one of the PixelID types, and an PixelID can be converted to a value.
However, a run-time value can not be converted to this compile time
type.


See:
 PixelIDToImageType

 ImageTypeToPixelID

 ImageTypeToPixelIDValue

 PixelIDToPixelIDValue


C++ includes: sitkPixelIDTypes.h
";


%feature("docstring") itk::simple::DualExecuteInternalAddressor "
C++ includes: sitkDetail.h
";


%feature("docstring") itk::simple::DualExecuteInternalVectorAddressor "

An addressor of ExecuteInternalCast to be utilized with registering
member functions with the factory.

C++ includes: sitkDetail.h
";


%feature("docstring") itk::simple::ExecuteInternalLabelImageAddressor "

An addressor of ExecuteInternal to be utilized with registering member
functions with the factory.

C++ includes: sitkDetail.h
";


%feature("docstring") itk::simple::ExecuteInternalVectorImageAddressor "

An addressor of ExecuteInternalCast to be utilized with registering
member functions with the factory.

C++ includes: sitkDetail.h
";


%feature("docstring") itk::simple::MemberFunctionAddressor "
C++ includes: sitkDetail.h
";


%feature("docstring") itk::simple::hash "
C++ includes: sitkMemberFunctionFactoryBase.h
";


%feature("docstring") itk::simple::hash< std::pair< S, T > > "
C++ includes: sitkMemberFunctionFactoryBase.h
";


%feature("docstring") itk::simple::hash< std::tuple< TupleArgs... > > "
C++ includes: sitkMemberFunctionFactoryBase.h
";


%feature("docstring") itk::simple::scope_exit "
C++ includes: sitkTemplateFunctions.h
";

%feature("docstring")  itk::simple::scope_exit::scope_exit "
";

%feature("docstring")  itk::simple::scope_exit::scope_exit "
";

%feature("docstring")  itk::simple::scope_exit::scope_exit "
";

%feature("docstring")  itk::simple::scope_exit::~scope_exit "
";
